The relative role of space and place has long been debated in geography. Yet modeling efforts applied to coupled human-natural systems seemingly favor models assuming continuous spatial relationships. We examine the relative importance of placebased hierarchical versus spatial clustering influences in tropical land use/cover change (LUCC). Guatemala was chosen as our study site given its high rural population growth and deforestation in recent decades. We test predictors of 2009 forest cover and forest cover change from 2001-2009 across Guatemala's 331 municipalities and 22 departments using spatial and multi-level statistical models. Our results indicate the emergence of several socio-economic predictors of LUCC regardless of model choice. Hierarchical model results suggest that significant differences exist at the municipal and departmental levels but largely maintain the magnitude and direction of single-level model coefficient estimates. They are also intervention-relevant since policies tend to be applicable to distinct political units rather than to continuous space. Spatial models complement hierarchical approaches by indicating where and to what magnitude significant negative and positive clustering associations emerge. Appreciating the comparative advantages and limitations of spatial and nested models enhances a holistic approach to geographical analysis of tropical LUCC and human-environment interactions.
Levins' Loop Analysis was developed for investigating the behavior of distributed, interconnected systems in which the interactions are not known well enough to completely model quantitatively, such as in many ecological communities. The presence or absence and the signs of the interactions between the subsystems may be all that is assumed in the model structure. The analysis focuses on the question of the qualitative stability, in the small, of the equilibrium points of the system as well as the possible first order shifts in these equilibrium points due to the influence of constantly acting disturbances. The Loop method is extended in this discussion to the qualitative analysis of the transient and long term tracking behavior of stable systems experiencing time-dependent and periodic disturbances.
The use of regression, ordination and dynamic ecosystem modelling in limnology is discussed by evaluating some of the vices and virtues of these techniques. Both general characteristics of the approaches and a few examples are used to stress the importance of analysis of the residuals for evaluation of the models. Not completely unexpected, a simple ordination model did not perform worse than a complicated ecosystem model, both applied to the same lake system. Both models are shown to fail for prediction purposes, mainly because the error variance in the data used for parameter estimation is large compared to the variance that can be explained.Integration of regression, multivariate analysis and dynamic ecosystem modelling, all followed by analysis of the residuals, is advised. Nested models are proposed as a solution for the problem of changing parameters and for the problem of parameters being different among lakes.
The problem of uncertainty in ecological modelling, in particular uncertainty of ecological data and uncertainty of expert knowledge, appears to be crucial. This paper presents some initial results obtained by investigation of this problem based on the fuzzy logic approach.
The ectoparasitic mite Varroa jacobsoni Oud. presently poses one of the most serious problems faced by keepers of honeybees Apis mellifera L. To help understand why the mite has become such a serious problem a population dynamics model using recently published data has been constructed. The simulation model has been built by linking together various aspects of the mites’ biology using computer software (ModelMaker®) in such a way that an initial population of mites can change daily over any period. The model predicts a yearly 12-fold increase in mite numbers or an intrinsic rate of daily increase of 0.021 during the presence of bee brood. This corresponds well with field data. Values derived from the model for behaviours such as drone preference (5.5–12 times) and phoretic period (4–11 days) are similar to those actually observed. Therefore, the model can be used to predict the number of mites within any colony and their subsequent development over any period. Since the daily development of both the live population and numbers of dead mites are predicted by the model it can be used as a mite population monitoring tool. The model predicts that the ratio of live to dead mites will change dramatically between periods when bee brood is present or absent. However, since the ratios were shown to be stable within the periods, the mite population can be estimated throughout the year by multiplying the daily mite drop by ≈250–500 or 20–40 when brood is absent or present, respectively. This will allow beekeepers to optimise their mite control strategy. The model also reveals the complex pattern in infestation levels that occurred throughout the year which was caused by the interactions between the bee and mite breeding cycles and will allow the role of bee viruses in the collapse of the colony to be studied in much greater detail.
Mass balanced models yield valuable information regarding ecological function and delivery of ecosystem services, but often rely on data collected well before many species were reduced to fractions of their original abundance. Lagoonal systems, such as Great South Bay (GSB), NY, sit on the interface of terrestrial and marine ecosystems and are prone to anthropogenic stressors but proximity to land also makes the presence of data regarding historic populations and structure more likely. To quantify over a century of ecosystem change, Ecopath models were developed for GSB at each of four time periods where commercial and scientific data exist: 1880s, 1930s, 1980s and 2000s. The results indicated that the GSB has experienced a decline in ecosystem maturity, loss of top keystone predators, a decline in connectivity to the ocean though the reduction of migratory species and increasing dominance of low trophic level organisms. These changes undermine the delivery of ecosystem services, increase conflicts over limited resources and suggest that present day restoration targets fail to recognize appropriate baselines. We discuss the role of stochastic events, which result in state changes that could be defined as regime shifts, and ecosystem connectivity to the long-term stability of lagoonal systems.Highlights► We constructed four mass balanced models of GSB, NY over different time periods. ► We assessed changes in ecosystem structure and maturity and shifts in keystone species. ► GSB experienced declines in ecosystem maturity and connectivity to the ocean. ► GSB has lost keystone species and become dominated by low trophic level organisms. ► These changes undermine ecosystem services and increase conflicts over resources.
The aim of this paper is to analyze the behavior of models which describe the population dynamics taking into account the subjectivity in the state variables or in the parameters. The models in this work have demographic and environmental fuzziness. The environmental fuzziness is presented using a life expectancy model where the fuzziness of parameters is considered. The demographic fuzziness is presented using the continuous Malthus and logistic discrete models. An outstanding result in this case is the emergence of new fixed points and bifurcation values to the discrete logistic model with subjective state variables in form of fuzzy sets. An interpretation is offered for this fact which differs from the deterministic one.
During the EU-project ECOMONT (Project No. ENV4-CT95-0179), which focuses on effects of land-use changes on mountain ecosystems, an experimental and modelling strategy at the leaf level was developed, which centres on the intimate relationship between leaf nitrogen content and net photosynthesis. Leaf nitrogen content reflects nutrient availability, shows characteristic seasonal dynamics and is of great importance for the analysis of intraspecific variability of gas exchange. The fully parameterised leaf model is capable of predicting net photosynthesis and stomatal conductance for any combination of microclimatic variables as well as any leaf nitrogen content. At the ECOMONT pilot research area Monte Bondone (Trentino/Italy) three grassland sites differing in land-use, a hay meadow, a pasture and an area abandoned since 35 years were selected as study sites. Leaf gas exchange characteristics and nitrogen contents of 13 species were studied and used for parameterising nitrogen sensitive leaf models. Independent data sets, diurnal courses of photosynthesis and stomatal conductance under the prevailing environmental conditions, were used for validation. A sensitivity analysis was performed in order to test the ability of the model to account for changes in leaf nitrogen content, varying leaf nitrogen content together with the environmental driving variables. These leaf models provide the physiological basis for scaling-up gas exchange from the leaf to the whole plant and canopy level and further to the landscape level.
Using a three-dimensional stochastic model of radionuclides in forests developed in Part I, this work simulates the long-term behavior of Cs-137 in forest soil. It is assumed that the behavior of Cs-137 in soils is driven by its advection and dispersion due to the infiltration of the soil solution, and its sorption to the soil matrix. As Cs-137 transport through soils is affected by its uptake and release by forest vegetation, a model of radiocesium behavior in forest vegetation is presented in Part III of this paper. To estimate the rate of infiltration of water through the soil, models are presented to estimate the hydrological cycle of the forest including infiltration, evapotranspiration, and the root uptake of water. The state transition probabilities for the random walk model of Cs-137 transport are then estimated using the models developed to predict the distribution of water in the forest. The random walk model is then tested using a base line scenario in which Cs-137 is deposited into a coniferous forest ecosystem.
Sensitivity and uncertainty analysis were conducted on a stretch of the Rhone River (France) with CASTEAUR, a model computing radionuclides transfers to abiotic and biotic components of fluvial ecosystems. The sensitivity of accumulation in omnivorous fish was analysed by constructing linear models between this output variable and some input parameters sets constructed using the Latin Hypercube sampling technique. When sedimentation occurred, the fish feeding ratio and the river suspended matter load explained the major part of the uncertainty associated to accumulation in fish. Results of an uncertainty analysis were also confronted to the accumulation measured in chubs (Leuciscus cephalus L.) of the Rhone River, downstream from a nuclear reprocessing plant. The model predictions and their associated uncertainties enclosed the observed radioactivity in fish, but only when suspended solids deposition was included in the model. We conclude from these observations that the sedimentary dynamics and the fish feeding habits should be properly characterized when assessing the impact of effluents in rivers using mathematical simulation models such as CASTEAUR.
A dynamic simulation model of salt accumulation on irrigated lands is presented. The original version of the model is part of a large-scale socio-economic model of irrigation-based regional development. The model introduced in this paper is a systemic one in the sense that it integrates four major sub-processes of rootzone salinization: irrigation, drainage, groundwater discharge and groundwater intrusion. It provides a comprehensive and general description of the long-term process of salt accumulation in lowlands under continuous irrigation practice, where irrigated lands are annually increased. Analysis of the model and simulation results reveal, under what conditions the salinity reaches alarming levels and with what strategies it can be controlled. For instance, in situations where the mixing of drainage water into irrigation water supplies is high, rootzone salinity quickly reaches alarming levels. More importantly, in this setting, the typical strategy of increasing the drainage in order to control the salinity level yields unprecedented exponentially growing salinity levels, a catastrophic result for the agriculture. The model structure can represent the basin wide salinization process on different geographical settings in agricultural development. In general, the model provides an experimental simulation platform, which can be used by the policy makers in the long term strategic management of large scale irrigation development projects. The model can also be of interest to the students and learners in teaching and research, in the related fields of environmental sciences.
Ecopath is mass-balance modeling approach that is widely used for incorporating ecosystem considerations into fisheries science. Up to now, users of Ecopath software who are constructing a model of a given area must carefully adjust input biomass, diets, and other parameters until the Ecopath parameterization is mass-balanced, a slow process leading to non-unique solutions. We present a new computer-automated iterative technique for mass-balancing Ecopath models which has the advantages of (1) reducing the lengthy process of and opportunity for encoding errors of the manual approach; (2) standardizing results for the same set of starting conditions; and (3) allowing exploration of alternative solutions, with consideration of the estimated confidence of each input parameter. Users can select random and/or gradient descent model perturbation of biomass and/or diet parameters, specify an objective (cost) function for optimization of the search, and modify decision logic, including simulated annealing. An objective function is defined to help target mass-balance solutions with minimum change to original input parameters. A Monte Carlo mode allows exploration of sensitivity to different starting conditions and random perturbations. The new procedure is implemented in the current version of the freely available Ecopath with Ecosim software (http://www.ecopath.org).
Models predicting species spatial distribution are increasingly applied to wildlife management issues, emphasising the need for reliable methods to evaluate the accuracy of their predictions. As many available datasets (e.g. museums, herbariums, atlas) do not provide reliable information about species absences, several presence-only based analyses have been developed. However, methods to evaluate the accuracy of their predictions are few and have never been validated. The aim of this paper is to compare existing and new presence-only evaluators to usual presence/absence measures.We use a reliable, diverse, presence/absence dataset of 114 plant species to test how common presence/absence indices (Kappa, MaxKappa, AUC, adjusted D2) compare to presence-only measures (AVI, CVI, Boyce index) for evaluating generalised linear models (GLM). Moreover we propose a new, threshold-independent evaluator, which we call “continuous Boyce index”. All indices were implemented in the BIOMAPPER software.We show that the presence-only evaluators are fairly correlated (ρ > 0.7) to the presence/absence ones. The Boyce indices are closer to AUC than to MaxKappa and are fairly insensitive to species prevalence. In addition, the Boyce indices provide predicted-to-expected ratio curves that offer further insights into the model quality: robustness, habitat suitability resolution and deviation from randomness. This information helps reclassifying predicted maps into meaningful habitat suitability classes. The continuous Boyce index is thus both a complement to usual evaluation of presence/absence models and a reliable measure of presence-only based predictions.
The high number of cases where pesticide residues have been found in groundwater during the last decade has enhanced the need for more knowledge about the fate of pesticides in soil. The purpose of the present study was to extend the knowledge of pesticide mineralisation in soil. Many publications have described the difficulties of finding a useful mathematical model for the description of pesticide mineralisation. In the present study a mathematical model is presented, which was useful for describing cometabolic mineralisation as well as metabolic mineralisation. On the basis of mineralisation studies of mecoprop in Danish soils, a predictive model, which described the mineralisation as a function of biological activity, soil texture, humus content and soil depth, was developed. The model was validated against mecoprop mineralisation studies in German soils and was shown to be very useful for the prediction of time for total mineralisation of mecoprop.
The Cantabrian Sea shelf ecosystem is described using a mass-balance model of trophic interactions, in order to understand the effects of the different fisheries that operate in this area. The study was based on a database of bottom trawl surveys, ICES stock assessment working groups, stomach analyses, fisheries research and was supplemented by published information. The model had 28 trophic groups corresponding to pelagic, demersal and benthic domains, also including detritus and fishery discards. The results indicated that the biomass and production of some groups would be unrealistic if they were independently estimated by single-species assessment approaches. Summaries are given to illustrate the flow distributions between groups. Strong relationships existed between the pelagic, demersal and benthic domains due to key groups, like zooplankton suprabenthic and horse mackerel, that transferred the flow from primary production to the upper trophic levels. Feeding pressure on phytoplankton was low and detritivorous species were an important component of the ecosystem.
Keystones are defined as relatively low biomass species with a structuring role in their food webs. Thus, identifying keystone species in a given ecosystem may be formulated as: (1) estimating the impact on the different elements of an ecosystem resulting from a small change to the biomass of the species to be evaluated for its ‘keystoneness’; and (2) deciding on the keystoneness of a given species as a function of both the impact estimated in (1) and its own biomass. Experimental quantification of interaction strength necessarily focus on few species, and require a priori assumptions on the importance of the interactions, which can bias the identification of keystone species. Moreover, empirical measurements, although very important, are expensive and time consuming and, owing to the spatio-temporal heterogeneity of habitats, physical conditions, and densities of organisms, published results tend to be case-specific and context-dependent.Although models can only represent but a caricature of the complexity of the real world, the modelling approach can be helpful since it allows overcoming some of the difficulties mentioned. Here we present an approach for estimating the keystoneness of the functional groups (species or group of species) of food web models. Network mixed trophic impact analysis, based on Leontief's economic input–output analysis, allows to express the relative change of biomasses in the food web that would result from an infinitesimal increase of the biomass of the observed group, thus identifying its total impact. The analysis of the mixed trophic impacts presented here was applied to a suite of mass-balance models, and the results allow us to rank functional groups by their keystoneness. Overall, we concluded that the straightforward methodology proposed here and the broad use of Ecopath with Ecosim (where mixed trophic impact analysis is implemented) together give a solid empirical basis for identification of keystone functional groups.
An artificial neural network (ANN), a data driven modelling approach, is proposed to predict the algal bloom dynamics of the coastal waters of Hong Kong. The commonly used back-propagation learning algorithm is employed for training the ANN. The modeling is based on (a) comprehensive biweekly water quality data at Tolo Harbour (1982–2000); and (b) 4-year set of weekly phytoplankton abundance data at Lamma Island (1996–2000). Algal biomass is represented as chlorophyll-a and cell concentration of Skeletonema at the two locations, respectively. Analysis of a large number of scenarios shows that the best agreement with observations is obtained by using merely the time-lagged algal dynamics as the network input. In contrast to previous findings with more complicated neural networks of algal blooms in freshwater systems, the present work suggests the algal concentration in the eutrophic sub-tropical coastal water is mainly dependent on the antecedent algal concentrations in the previous 1–2 weeks. This finding is also supported by an interpretation of the neural networks’ weights. Through a systematic analysis of network performance, it is shown that previous reports of predictability of algal dynamics by ANN are erroneous in that ‘future data’ have been used to drive the network prediction. In addition, a novel real time forecast of coastal algal blooms based on weekly data at Lamma is presented. Our study shows that an ANN model with a small number of input variables is able to capture trends of algal dynamics, but data with a minimum sampling interval of 1 week is necessary. However, the sufficiency of the weekly sampling for real time predictions using ANN models needs to be further evaluated against longer weekly data sets as they become available.
Geographic information about historical and possible future land-use changes is necessary for various kinds of ecological models. Introduced here is a global scenario which is regionalized on a 2.5° grid. The scenario consists of two parts. One part is historical pertaining to the period 1860–1980. It is based on available data concerning site and area of land-use changes. In contrast, the second part of the scenario includes a probability estimate for land-use development and distribution for the assumed period, 1981–2500. Here, on grid-element level, clearings occur due to logistic functions. The turning point for each logistic function is calculated by the global clearing function. The sequence in which the grid elements are cleared is determined by clearing probability. This is the product of four individual probability factors: the intensity of land use in neighbouring grid elements, natural productivity, soil fertility, and historical land-use changes.
We applied detailed forest reconstructions measured on broad-scale plot grids to initialize forest simulation modeling in 1880 and modeled spatially explicit changes in canopy fuels (canopy biomass, canopy bulk density, species composition) and potential fire behavior (crowning index) through 2040, a 160-year period. The study sites spanned a 500-m elevational gradient from ponderosa pine forest through higher-elevation mixed conifer, aspen, and spruce-fir forests on the North Rim of Grand Canyon National Park in northern Arizona. The simulations were relatively accurate, as assessed by comparing the simulation output in the year 2000 with field data collected in 1997–2001, because a regionally calibrated simulator was used (Central Rockies variant of the Forest Vegetation Simulator) and because we added regeneration by species and density in the correct historical sequence. Canopy biomass increased at all sites, rising an average of 122% at the low-elevation sites and 279% at the high-elevation sites. The intermediate-elevation site, where mixed conifer vegetation predominated, began with the highest canopy biomass in 1880 but had the lowest increase through 2040 (39%). Canopy bulk density increased roughly in parallel with canopy biomass; however, density values were considered less accurate in non-contemporary dates because they were based on assumptions about canopy volume. Species composition of canopy fuels was consistent at low elevation (ponderosa pine) but shifted strongly toward mesic species at higher elevations, where ponderosa pine declined in absolute as well as relative terms. Potential crown fire behavior was assessed with the Nexus model in terms of crowning index (CI), the windspeed required to sustain active canopy burning. CI values decreased 23–80% over the modeled period. Canopy fuel and CI values were mapped across the entire North Rim landscape. At a threshold windspeed of 45 km/h, only 6% of the landscape was susceptible to active crown fire in 1880 but 33% was susceptible by 2000. Implications of the changes over time and space include altered contemporary habitats and the high likelihood of rapid, broad-scale disturbance by fire. If managers choose to intervene to reduce canopy fuel mass and continuity, actions should be guided by the distinct ecological attributes of the different forest types.
The availability of detailed environmental data, together with inexpensive and powerful computers, has fueled a rapid increase in predictive modeling of species environmental requirements and geographic distributions. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, absence data are not available for most species. In this paper, we introduce the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data. Maxent is a general-purpose machine learning method with a simple and precise mathematical formulation, and it has a number of aspects that make it well-suited for species distribution modeling. In order to investigate the efficacy of the method, here we perform a continental-scale case study using two Neotropical mammals: a lowland species of sloth, Bradypus variegatus, and a small montane murid rodent, Microryzomys minutus. We compared Maxent predictions with those of a commonly used presence-only modeling method, the Genetic Algorithm for Rule-Set Prediction (GARP). We made predictions on 10 random subsets of the occurrence records for both species, and then used the remaining localities for testing. Both algorithms provided reasonable estimates of the species’ range, far superior to the shaded outline maps available in field guides. All models were significantly better than random in both binomial tests of omission and receiver operating characteristic (ROC) analyses. The area under the ROC curve (AUC) was almost always higher for Maxent, indicating better discrimination of suitable versus unsuitable areas for the species. The Maxent modeling approach can be used in its present form for many applications with presence-only datasets, and merits further research and development.
Regime shifts, consisting of decadal-scale oscillations in atmosphere–ocean systems, have recently been the focus of many marine ecosystem studies. These ‘regime shifts’ effect the sea surface temperature and mixed layer depth (MLD), changing the environment for marine ecosystems. We simulated changes in the marine ecosystem caused by interdecadal climate variability, using data from 1948 to 2002 to drive an ecosystem model, NEMURO, embedded in a global three-dimensional physical–biological coupled model, ‘3D-NEMURO’.The results were consistent with observations. Comparing before and after the late 1970s regime shift, primary production and biomass of phytoplankton increased in the north central Pacific but decreased in the sub-tropical northwestern and eastern Pacific. This corresponds to the Pacific decadal oscillation (PDO) index that indicates interdecadal climate variability in the sub-tropical and tropical Pacific. In the north central Pacific, biomass correlated positively with PDO while that in the north eastern and western Pacific correlated negatively with PDO.
Land use change, natural disturbance, and climate change directly alter ecosystem productivity and carbon stock level. The estimation of ecosystem carbon dynamics depends on the quality of land cover change data and the effectiveness of the ecosystem models that represent the vegetation growth processes and disturbance effects. We used the Integrated Biosphere Simulator (IBIS) and a set of 30- to 60-m resolution fire and land cover change data to examine the carbon changes of California's forests, shrublands, and grasslands. Simulation results indicate that during 1951–2000, the net primary productivity (NPP) increased by 7%, from 72.2 to 77.1 Tg C yr−1 (1 teragram = 1012 g), mainly due to CO2 fertilization, since the climate hardly changed during this period. Similarly, heterotrophic respiration increased by 5%, from 69.4 to 73.1 Tg C yr−1, mainly due to increased forest soil carbon and temperature. Net ecosystem production (NEP) was highly variable in the 50-year period but on average equalled 3.0 Tg C yr−1 (total of 149 Tg C). As with NEP, the net biome production (NBP) was also highly variable but averaged −0.55 Tg C yr−1 (total of –27.3 Tg C) because NBP in the 1980s was very low (–5.34 Tg C yr−1). During the study period, a total of 126 Tg carbon were removed by logging and land use change, and 50 Tg carbon were directly removed by wildland fires. For carbon pools, the estimated total living upper canopy (tree) biomass decreased from 928 to 834 Tg C, and the understory (including shrub and grass) biomass increased from 59 to 63 Tg C. Soil carbon and dead biomass carbon increased from 1136 to 1197 Tg C.
Using a nitrogen balance model based on statistical data, nitrogen loads due to food production and consumption and energy production were estimated for each 0.5° × 0.5° grid cell in the 13 countries of eastern Asia from 1961 to 2002. Groundwater quality was estimated with a simple first-order reaction model. Total nitrogen load, including natural nitrogen fixation, increased by a factor of 3.8 between 1961 and 2002 (with an increase by a factor of 15.6 due to crop production, 14.1 due to livestock waste, 2.4 due to human waste, and 4.5 due to energy production). The present estimate of average nitrogen load is 3.9 t km−2 yr−1. Our model indicated high nitrogen concentrations in groundwater in eastern and northeastern China and some areas of Republic of Korea and Japan. In many countries, per capita food consumption and the common logarithm of per capita NOx emission show significant linear relationships with the common logarithm of per capita GDP. Based on these relationships, we predicted food demands, fertilizer demand, NOx emission, and the resulting nitrogen loads for the next 18 years. Our model predicts that in 2020, the total nitrogen load will be 1.3–1.6 times the present load across the entire region according to several scenarios on economic growth and population increase, with various patterns of change in the different countries.
The human impact on quasi-natural systems was analyzed in constructing quantitative trophic models describing the changing situation of the Laguna de Bay ecosystem's aquatic compartment for two time frames. The trophic model developed for the late 1960s described the biological food chain during the pre-fishpen period. On the other hand, the model for the early 1980s emphasized the effect of introducing milkfish (Chanos chanos) in fishpens, with special reference on its impact on the primary productivity of the lake's ecosystem. The sum of all flows to detritus for the entire system was 3.2 times lower in 1980 than in 1968. Due to the presence of milkfish in fishpens, the total net primary production decreased twofold during the 1980s. The development of milkfish as one central part of the lake's ecosystem was the result of the interplay of several factors related to commercial interests, political affairs, climatological changes and biological responses involved in the overproduction of milkfish during the 1980s. Preliminary analysis of the nitrate and phosphate loadings from 1973 to 1982 were considered as important indicators in analyzing pollution-related lake eutrophication. Since the 1840s, the status of the Laguna de Bay ecosystem has demonstrated the steadily growing impact of human activities, especially on the dynamics of the lake's trophic structure.
The northern Benguela ecosystem has been overfished and physically challenged over the past three decades. Ecopath with Ecosim was used to construct three ecosystem models (1971–1977, 1980–1989, and 1990–1995) and to compare differences in ecosystem structure. In the 1970s, the system sustained high catches, and had large populations of a few planktivorous fish. In the 1980s, the planktivorous fish species were expanded (horse mackerel, mesopelagic fish, and other small pelagics), although anchovy and sardine biomass was reduced. Catches remained high in the 1980s and the system was well connected. In the 1990s, the system was severely stressed, catches were much lower and omnivory was reduced. Most of the energy flowed through few pathways in the 1990s, and the energy was not transferred as efficiently up the trophic chain as in the 1980s. The fishery operated at the highest trophic level during the 1980s and there are some indications of “fishing down the foodweb” in this ecosystem between the 1980s and the 1990s. The high catches of sardine and hake in the 1970s are reflected in the high primary production required (PPR) by those compartments; the high catches of horse mackerel in the 1980s are shown by the high PPR for horse mackerel. The overall PPR for the fishery was highest in the 1980s, when the system was fished at nearly the same intensity as the 1970s, but the species taken were from higher trophic levels, requiring larger concentrations of primary production for their own existence. The importance of ecosystem–environmental interactions are highlighted by the abundance of horse mackerel, mesopelagics, small pelagics, and hake in the 1980s and the reduced biomass of most species in the 1990s, not only due to overfishing, but also due to the Benguela Niño that occurred in 1995. The system changed from an efficient ecosystem dominated by only two planktivores (anchovy and sardine) in the 1970s, to a system of large resilience and a varied planktivore population during the 1980s. However, the system’s resilience was lower, but its connectance was higher in the 1990s, where sardine was making a comeback and the marine mammals were doing well until the Benguela Niño reduced the system to a state of lower maturity.
The spatial pattern of forest fire locations is of interest for fire occurrence prediction and for understanding the role of fire in landscape processes. A spatial statistical analysis of lightning-caused fires in the province of Ontario, between 1976 and 1998, was carried out to investigate the spatial pattern of fires, the way they depart from randomness, and the scales at which spatial correlation occurs. Fire locations were found to be spatially clustered. Kernel estimation of the spatial pattern of lightning strikes on days when the dryness of the forest floor exceeded a designated threshold yielded clusters in the same areas as the lightning fire clusters.
Forest fragmentation threatens biodiversity in one of the last remaining temperate rainforests that occur in South America. We study the current and future impacts of fragmentation on spatial configuration of forest habitats at the landscape level time in southern Chile. For this purpose, we identify the geophysical variables (“pattern drivers”) that explain the spatial patterns of forest loss and fragmentation between 1976 and 1999 using both a GIS-based land-use change model (GEOMOD) and spatially explicit logistic regression. Then, we project where and how much forest fragmentation will occur in the future by extrapolation of the current rate of deforestation to 2010 and 2020. Both modeling approaches showed consistent and complementary results in terms of the pattern drivers that were most related to deforestation. Between 1976 and 1999, forest fragmentation has occurred mainly from the edges of small fragments situated on gentle slopes (less than 10°) and far away from rivers. We predict that patch density will decline from 2010 to 2020, and that total forest interior area and patch proximity will further decline as a result of forest fragmentation. Drivers identified by these approaches suggest that deforestation is associated with observed local socio-economic activities such as clearance of forest for pasture and crops and forest logging for fuelwood.
An ecosystem model representing the continental shelf and upper slope of the South Catalan Sea (NW Mediterranean) is calibrated and fitted to the available time series data from 1978 to 2003. We use a process-oriented model to explore the extent to which changes in marine resources and the ecosystem were driven by trophic interactions, environmental factors and fishing activities. Fishing effort and fishing mortality are used to drive the model, while observed (absolute and relative) biomasses and catches are compared with the predicted results. A reduction in the sum of the squared deviations of the observed and predicted values of the biomass is used as a metric for calibrating and assessing the fit of the model. A posteriori trophodynamic indicators are used to explore the ecosystem's structural and functional changes from 1978 to 2003, and a generalized least squares regression is used to assess the significance of the predicted trends. In general, a high proportion of the variability in the time series data is explained by the main trophic interactions (37–53%), fishing activities (14%), and indirectly by considering the environment (6–16%), as driving factors. The model's predictions match satisfactorily with the yearly data on the biomass for anglerfish, adult hake, demersal sharks, anchovy and mackerel, which show a statistically significant decrease over time, while the biomass of flatfish and seabirds are observed to increase. Catch data show a significant decrease in anglerfish, demersal sharks, anchovy and sardine, while there is an increase in red mullet, flatfish, juvenile hake and horse mackerel. These changes in biomass are predicted to have direct and indirect impacts on the ecosystem mediated by the trophic web, such as the proliferation of non-commercial species with lower trophic levels (e.g., benthic invertebrates) or higher turnover rates (e.g., cephalopods and benthopelagic fish). This is consistent with anecdotal information from the Mediterranean and is likely caused by trophic cascades due to the removal of demersal and pelagic higher trophic level organisms (predator release), and a decrease in small pelagic fish (competitor release). Trophodynamic indicators suggest a degradation pattern over time: both the mean trophic level of the community (mTLco, excluding primary producers and detritus) and a modified version of Kempton's index of biodiversity decrease with time, while the total flow to detritus and the loss of production due to fishing increase from 1978 to 2003. Additionally, the demersal/pelagic ratio increases due to an overall decrease in the abundance of small pelagic fish in the ecosystem.
The coastal ecosystem of the Pearl River Estuary (PRE) has been overfished and received a high level of combined pollution since the 1980s. Ecopath with Ecosim was used to construct two ecosystem models (for 1981 and 1998) to characterize the food web structure and functioning of the ecosystem. Pedigree work and simple sensitivity analysis were carried out to evaluate the quality of data and the uncertainty of the models. The two models seem reliable with regards to input data of good quality. Comparing the variations of outputs of these two models aimed to facilitate assessment of changes of the ecosystem during the past two decades.
Replicate mass-balanced solutions to Ecopath models describing carbon-based trophic structures and flows were developed for the Lake Ontario offshore food web before and after invasion-induced disruption. The food webs link two pathways of energy and matter flow: the grazing chain (phytoplankton–zooplankton-fish) and the microbial loop (bacteria–protozoans) and include 19 species-groups and three detrital groups. Mass-balance was achieved by using constrained optimization techniques to randomly vary initial estimates of biomass and diet composition. After the invasion, production declined for all trophic levels and species-groups except Chinook salmon. The trophic level (TL) increased for smelt, adult sculpin, adult alewife and Chinook salmon. Changes to ecotrophic efficiencies indicate a reduction in phytoplankton grazing, increased predation pressure on Mysis, adult smelt and alewife and decreased predation pressure on protozoans. Specific resource to consumer TTE changed; increasing for protozoans (8.0–11.5%), Mysis (0.6–1.0%), and Chinook salmon (1.0–2.3%) and other salmonines (0.4–0.5%) and decreasing for zooplankton (20.2–15.1%), prey-fish (9.7–8.8%), and benthos (1.7–0.6%). Direct trophic influences of recent invasive species were low. The synchrony of the decline in PP and species-group production indicates strong bottom-up influence. Mass balance required an increase of two to threefold in lower trophic level biomass and production, confirming a previously observed paradoxical deficit in lower trophic level production. Analysis of food web changes suggest hypotheses that may apply to other similar large pelagic systems including, (1) as pelagic primary productivity declines, overgrazing of zooplankton results in an increase in protozoan production and a loss of trophic transfer efficiency, (2) habitat and food web changes increased Mysis predation on Diporeia and contributed to their recent decline, and (3) production of Chinook salmon, the primary piscivore, was uncoupled from pelagic production processes. This study demonstrates the value of food web models to better understand the impact of invasive species and to develop novel hypotheses concerning trophic influences.
Trophic interactions and community structure of commercial fishery species off Central Chile (33°–39°S) were analyzed and compared for 1992 and 1998 by ecotrophic modelling, using the Ecopath modelling software. The model encompasses the fishery, pinnipeds (sea lions), small pelagic fish (anchovy, pilchard), medium-sized pelagic fish (horse mackerel), demersal fish (e.g. Chilean hake, black conger), benthic invertebrates (carrot prawn, yellow prawn), and other groups such as zooplankton, phytoplankton, and detritus. Input information for the model was gathered from published and unpublished reports and our own estimates. Also, the effects of fishing and predation on fishery resources and on the most important components of the system were investigated, within an ecotrophic framework.Predators consumed the greater part of the production of the most important fishery resources, particularly juvenile stages, and the fishery removed a large fraction of adult production. Mortality by predation is an important component of natural mortality, especially in recruit and prerecruit groups. Analysis of direct and indirect trophic impact shows that adult Chilean hake have a negative impact on juvenile Chilean hake through cannibalism, and on pilchard, anchovy, and carrot prawn through predation. Also, fishing has a strong impact on fishery resources, such as Chilean hake, pilchard, and anchovy. Total biomass in 1998 was 1.5 times higher than in 1992. However, total catches in 1998 were about 80% of those in 1992. Changes in biomass and total yields of the system between 1992 and 1998 can be observed in such properties as total flows, consumption, respiration, and production. It is concluded that ecotrophic modelling is an useful tool for fishery management, since it can improve our understanding of the predator–prey interactions among the exploited (fishery resources) and unexploited but potential fishery resources of the system.
We used the one-dimensional DYRESM–CAEDYM model to elucidate and quantify the influence of the external phosphorus loading on ecosystem dynamics in moderately deep Lake Ravn, which is situated in an agricultural landscape in Denmark. Model simulations were used to quantify the extent to which the external phosphorus loading must be reduced to meet upcoming lake ecological quality requirements according to the European Union Water Framework Directive (WFD). The model generally showed good agreement with observed data for temperature and oxygen from the epilimnion and hypolimnion during the calibration period (7 years) as well as the validation period (5 years); although peaks of oxygen concentrations in epilimnion during late spring often were underestimated. Phosphate and total phosphorus (TP) concentrations were generally well reproduced in both the epilimnion and hypolimnion, though hypolimnetic phosphorus was occasionally underestimated in late summer. There was also good agreement between monitored data and modelled biomass of diatoms and dinoflagellates as well as the zooplankton biomass of cladocerans and calanoid copepods, although the timing of biomass peaks occasionally deviated from observations. Root-mean-square-errors (RMSE), used to quantify the model error, were overall similar for the calibration and the validation period. Simulations of scenarios with a reduced external TP loading suggest that a substantial reduction (40–50%) of the TP loading is required if phytoplankton biomass is to drop to a level sufficiently low to meet the proposed WFD requirements (summer average <6.5 μg chlorophyll a l−1). The predicted outcomes of considerable loading reductions should, however, be treated with some caution, as the conceptual model in this study could not fully account for the changes in trophic structure occurring at radically reduced TP loading. This particularly applies to changes in the fish stock, which may have extensive cascading effects via increased zooplankton grazing on the phytoplankton when external TP loading is reduced. This would most likely lead to higher transparency than that predicted by the model. To further improve the reliability of the predicted outcomes of various model scenarios, future work should include a series of test simulations also including fish predation. Today, fish predation is available in the DYRESM–CAEDYM model as well as other dynamic lake models; however, only sparse data is available on fish stock dynamics in Lake Ravn (as for most other lakes). An applicable test of the fish algorithms in today’s dynamic models will require reliable estimates of fish stock biomass in the study lakes during both the model calibration and validation period.
Sensitivity analysis consists of an integral and important validatory check of a computer simulation model before the code is used in performing any kind of analysis operation. The present paper demonstrates the use of a relatively new method and tool for conducting global sensitivity analysis (GSA) for environmental models, providing simultaneously the first GSA study of the widely used 1d soil–vegetation–atmospheric transfer (SVAT) model named SimSphere. A software platform called the Gaussian emulation machine for sensitivity analysis (GEM SA), which has been developed for performing a GSA via Bayesian theory, is applied to SimSphere model in order to identify the most responsive model inputs to the simulation of key model outputs, detect their interactions and derive absolute sensitivity measures concerning the model structure. This study is also very timely in that, use of this particular SVAT model is currently being considered to be used in a scheme being developed for the operational retrieval of the soil surface moisture content by National Polar-orbiting Operational Environmental Satellite System (NPOESS), in a series of satellite platforms that are due to be launched in the next 12 years starting from 2016.The employed GSA method was found capable of identifying the most responsive SimSphere inputs and also of capturing their key interactions for each of the simulated target quantities on which the GSA was conducted. The most sensitive model inputs were the topography parameters (slope, aspect) as well as the fractional vegetation cover and soil surface moisture availability. The implications of these findings for the future use of SimSphere are discussed.
Soil carbon (C) models are important tools for examining complex interactions between climate, crop and soil management practices, and to evaluate the long-term effects of management practices on C-storage potential in soils. CQESTR is a process-based carbon balance model that relates crop residue additions and crop and soil management to soil organic matter (SOM) accretion or loss. This model was developed for national use in U.S and calibrated initially in the Pacific Northwest. Our objectives were: (i) to revise the model, making it more applicable for wider geographic areas including potential international application, by modifying the thermal effect and incorporating soil texture and drainage effects, and (ii) to recalibrate and validate it for an extended range of soil properties and climate conditions. The current version of CQESTR (v. 2.0) is presented with the algorithms necessary to simulate SOM at field scale. Input data for SOM calculation include crop rotation, aboveground and belowground biomass additions, tillage, weather, and the nitrogen content of crop residues and any organic amendments. The model was validated with long-term data from across North America. Regression analysis of 306 pairs of predicted and measured SOM data under diverse climate, soil texture and drainage classes, and agronomic practices at 13 agricultural sites having a range of SOM (7.3–57.9 g SOM kg−1), resulted in a linear relationship with an r2 of 0.95 (P < 0.0001) and a 95% confidence interval of 4.3 g SOM kg−1. Using the same data the version 1.0 of CQESTR had an r2 of 0.71 with a 95% confidence interval of 5.5 g SOM kg−1. The model can be used as a tool to predict and evaluate SOM changes from various management practices and offers the potential to estimate C accretion required for C credits.
Rectangular grids are frequently used in spatial simulations, often with nearest neighbour interactions, but each cell has diagonal as well as orthogonal nearest neighbours. Here, a simple, abstract model of weed spread demonstrates that the relative strength of diagonal and orthogonal interactions affects simulation outcomes, by determining the threshold conditions required for spread from isolated and aggregated colonized cells. The relative strength of diagonal and orthogonal interactions implicitly represents the range of interaction processes. The von Neumann neighbourhood, which has no diagonal interactions, represents processes with zero or negligible range, such as contact processes; increasing the relative strength of diagonal interactions represents processes with increasing range, such as seed dispersal. Diagonal interactions are only likely to equal orthogonal interactions for processes with ranges that allow significant interactions beyond the nearest neighbours of a cell. Thus, the Moore neighbourhood of diagonal and orthogonal nearest neighbours with equal weight may be considered an inaccurate approximation to a larger neighbourhood. Accurate diagonal and orthogonal nearest neighbour interactions can be calculated by a method proposed by Buffon in the 18th century. This method is also useful for calculating the impacts of rescaling a grid on intercellular interactions. If the area represented by each cell in the grid is increased, diagonal interactions should be reduced more than orthogonal interactions. In a rectangular grid, setting diagonal interactions to half the strength of orthogonal interactions can achieve a good match to an equivalent simulation in a hexagonal grid in some cases, but not always.
Ecological Modelling publishes from time to time editorials, that review the development of the journal: after 50 volumes, after 100 volumes and at our 25 years anniversary. This time we celebrate 30 years anniversary and focus on the time 2000–2005, corresponding to the volumes 126–185. Sixty volumes – almost one third of all volumes of Ecological Modelling! We will review the development by means of statistics as we have done previously but this time we will also try to look behind the statistics and try to assess the development in the scientific fields of ecosystem theory (systems ecology) and of ecological modelling. What are the recent research focus in these two subfield of ecology?
The individual-based (aka agent-based) approach is now well established in ecological modeling. Traditionally, most applications have been to organisms at higher trophic levels, where the importance of population heterogeneity (intra-population variability), complete life cycles and behavior adapted to internal and external conditions has been recognized for some time. However, advances in molecular biology and biochemistry have brought about an increase in the application of individual-based modeling (IBM) to microbes as well. This literature review summarizes 46 IBM papers for bacteria in wastewater treatment plants, phytoplankton in ocean and inland waters, bacteria in biofilms, bacteria in food and other environs, and “digital organisms” and “domesticated computer viruses” in silico. The use of IBM in these applications was motivated by population heterogeneity (45%), emergence (24%), absence of a continuum (5%), and other unknown reasons (26%). In general, the challenges and concepts of IBM modeling for microbes and higher trophic levels are similar. However, there are differences in the microbe population dynamics and their environment that create somewhat different challenges, which have led to somewhat different modeling concepts. Several topics are discussed, including producing, maintaining and changing population heterogeneity (different life histories, internal variability, positive feedback, inter-generation memory), dealing with very large numbers of individuals (different up-scaling methods, including representative space vs. super-individual, number vs. biomass based, discrete vs. continuous kinetics, various agent accounting methods), handling space, simulating interactions with the extracellular environment (hybrid Eulerian–Lagrangian approach), modeling agent–agent interaction (self-shading, predation, shoving) and passive transport (random walk with spatially variable diffusivity, well-mixed reactors). Overall, the literature indicates that the application of IBM to microbes is developing into a mature field. However, several challenges remain, including simulating various types of agent–agent interactions (formation and function of colonies or filaments, sexual reproduction) and even smaller individuals (viruses, genes). Further increases in intracellular detail and complexity in microbe IBMs may be considered the combination of systems biology and systems ecology, or the new field of systems bioecology.
Trophic interactions and the relevance of the “classical” (CFW) versus the “microbial” (MFW) food webs were studied in the upwelling system of Antofagasta (23°S), northern Humboldt Current System (HCS) off Chile. Biological and ecological data gathered from the study area during 1996 and 1999–2002 and complementary data from the literature were analysed using the Ecopath with Ecosim software version 5.0 (EwE). The model includes the following functional groups: Detritus, dissolved organic matter (DOM), bacteria, phytoplankton, appendicularians, salps, calanoid copepods, cyclopoid copepods, chaetognaths, ctenophores, clupeiform fishes.
A method of sensitivity analysis for stochastic population models is proposed in which logistic regression is used to relate the probability of population decline to the parameters of the model. The regression equation serves to summarise the effects of different model parameters and interactions. The method was applied to a model of the helmeted honeyeater. The results may be used to place bounds on the predictions of the model. The predicted risk of population decline within the next 50 years is subject to substantial error.
Seagrasses provide a physical connection between the water column and sediments by transporting photosynthetic- and seawater-derived oxygen to their roots and rhizomes. In this paper, we present a single-shoot reaction-transport model that incorporates the biological, chemical and physical processes in the water column, seagrass plant, and sediments and that simulates oxygen and hydrogen sulfide dynamics in the system. The model reproduces oxygen and sulfide patterns observed in laboratory manipulations and field measurements of Thalassia testudinum and Zostera marina. Model results reinforce experimental conclusions that (1) meristem oxygen is tightly coupled to water column oxygen and diel patterns of sunlight, (2) sediment sulfide enters the plant when plant tissues are hypoxic, and (3) internal sulfide is rapidly depleted once oxic seawaters are re-established or with the onset of photosynthesis. Sensitivity analysis further emphasizes that water column oxygen concentration has a strong influence on the minimum oxygen concentration and maximum sulfide concentration in the meristem at night. The model indicates that diffusion is the dominant transport process in the lacunae, though advective mass flow can account for nearly a quarter of oxygen transport during periods of increasing sunlight. In the model, biological sulfide oxidation and plant dissolved organic carbon exudation both play significant roles in determining patterns of sediment oxygen consumption and sulfide intrusion into the plant.
Like many ecosystems that of Port Phillip Bay (Australia) shows spatial and temporal variability. As such, we have constructed a spatially structured dynamic model (the full model) to describe it. However, such complex models are very difficult to design and analyse. We have derived earlier a simple model of a semi-enclosed marine ecosystem, whose analytical solution has allowed the determination of the dependence of major variables and fluxes on model formulation and parameter values. Here we apply this simplified model to the analysis of responses to changes in nutrient loads and zooplankton mortality formulation (trophic closure). Output of the simple model compares well with output from the full model (averaged in space and time) at current and moderately elevated nutrient loads; but at highly elevated loads, there is a breakdown in the linkage between the predictions of the two models due to the role of spatial variation in moderating ecosystem responses. Observations obtained from Port Phillip Bay are then used to select the most realistic zooplankton mortality formulation of this bay. Observed chlorophyll concentration supports a quadratic over a linear zooplankton mortality model. Observation of phytoplankton size fractions indicate that in productive waters of the bay picophytoplankton are grazer controlled, while microphytoplankton escape grazer control; analytical solutions show how rather subtle differences in parameter values allow this to occur. This analysis tool allows the prediction of the effects of changes to model formulation, parameter values and external forcing on the full model. However, the simple model is not intended to be, or used as, a realistic model in itself; rather it is a design tool that has been used for the development of more detailed models.
A two-step methodology is presented for long-term eco-hydrodynamic simulation of a dendritic reservoir that can be subdivided into many interacting subsystems. This approach provides a balance between spatial resolution and simulation time extent. The first step aims at defining the exchange mass and water fluxes among basins. The second step is the eco-hydrodynamic modelling of the subsystems. This methodology is applied to Rapel reservoir, located in central Chile, which can be subdivided into three distinct basins. For this application, a 2D depth-averaged model is used to define exchange fluxes at the basin confluence, while a 1D, horizontally-averaged, vertically resolving model is used to simulate the hydrodynamics and biochemical behaviour of each basin. Dimensional analysis is introduced to analyse the water quality simulations and to determine whether internal processes or external loading are dominant and better explain the measured differences in phytoplankton biomass among the basins. The product of biomass growth rate and basin retention time is identified as an important dimensionless parameter describing the associated dynamics.
A two-dimensional model (2DLEAF) of leaf photosynthesis and transpiration has been developed that explicitly accounts for gas diffusion through the boundary layer and the intercellular space as well as for stomatal regulation. The model has been validated for tomato. It was used to study the effect of stomatal density on photosynthesis and transpiration rate. It has been demonstrated by varying stomatal density in the model that the stomatal density measured on tomato leaves provides the maximal photosynthesis rate for both 300 and 600 μl 1−1 [CO2]. The transpiration rate varied in direct proportion to stomatal density at all values of stomatal aperture, but transpiration efficiency (photosynthesis rate/transpiration rate) was higher at 600 μl 1−1 [CO2] with a normal stomatal density than at 300 μl 1−1 [CO2] with a stomatal density reduced 25%. Such calculations with 2DLEAF can be useful for analysis of contradicting data presented in publications on possible changes in stomatal density in a future high [CO2] atmosphere.
We developed a 3D ecosystem-biogeochemical model based on NEMURO (North Pacific Ecosystem Model Used for Regional Oceanography) and applied it to the western North Pacific in order to predict the effects of global warming on ecosystem dynamics and biogeochemical cycles. Using datasets of observed climatology and simulated fields according to a global warming scenario, IS92a (CO-AGCM developed by CCSR/NIES) as boundary conditions for our ecosystem model, we conducted present-day and global warming experiments and compared their results. Model results in the global warming experiment show increases in vertical stratification due to rising temperatures. As a result, the predicted nutrient and chlorophyll-a concentrations in the surface water decrease at the end of the 21st century, and the dominant phytoplankton group shifts from diatoms to other small phytoplankton. The P/B ratio slightly increases from that in the present as a result of favorable temperature conditions, although nutrient conditions become worse. The increase in the P/B ratio causes increases in the NPP and GPP, although new and export productions decrease. Increases in the regeneration rates (i.e., decrease in the e-ratio) also contribute to increases in NPP and GPP through nutrient supplies within the surface water. Changes in seasonal variations of biomass and the dominant phytoplankton group in the subarctic–subtropical transition region associated with the global warming are large in all regions. In the global warming scenario, the onset of the diatom spring bloom is predicted to take place 1.5 month earlier than in the present-day simulation due to strengthened stratification. The maximum biomass in the spring bloom is predicted to decrease drastically compared to the present due to the decreases in nutrient concentration. In contrast, the biomass maximum of the other small phytoplankton at the end of the diatom spring bloom is the same as the present, because they can adapt to the low nutrient conditions due to their small half-saturation constant. Therefore, a change in the dominant phytoplankton group appears notably at the end of spring bloom. Since the present nutrient concentrations and phytoplankton biomass from summer to winter are low compared with those in spring, these changes associated with the global warming are small. That is, it is interesting that the changes do not occur uniformly in all seasons, but occur dramatically at the end of the spring and in the fall bloom.
The function and dynamics of savanna ecosystems result from complex interactions and feedbacks between grasses and trees, involving numerous processes (i.e. competition for light, water and nutrients, fire, and herbivory). These interactions are characterised by strong relationships between vegetation structure and function. Given the heterogeneous structure of savannas, modelling appears as a convenient approach to study tree–grass interactions. Most current models that describe carbon and water fluxes are not spatially explicit, which restricts their ability to simulate plant interactions at small scales in heterogeneous ecosystems. We present here a new 3D process-based model called TREEGRASS. The model aims at predicting, in heterogeneous tree–grass systems, plant individual radiation, carbon and water fluxes at a local spatial scale. It is run at a daily time-step over periods ranging from one to a few years. The model includes (i) a 3D mechanistic submodel simulating radiation and energy (i.e. transpiration) budgets; (ii) a soil water balance submodel, and (iii) a physiologically based submodel of primary production and leaf area development. The ability of TREEGRASS to predict the seasonal courses of grass dead and leaf mass, soil water content and light regime as observed in the field has been tested for grassy and shrubby areas of Lamto savannas (Ivory Coast). Simulations showed that the spatial distribution of primary production can be strongly affected by the spatial vegetation structure. Potential applications involve predicting net primary production and water balance from the individual to the ecosystem and from the day to the annual vegetation cycle (e.g. effects of tree spatial patterns on carbon and water fluxes at the ecosystem level).
A 3D model of tree architecture is developed to simulate the photosynthesis of peach trees. The model uses a simple graphical procedure to compute the light interception and the corresponding photosynthesis. It allows to consider complex canopy arrangements, and is used to simulate the photosynthesis of laterals, trees and orchards. Results showed that the photosynthesis varied from 5 to 15 μmol m−2 s−1 between laterals of different architectures. The photosynthesis per unit leaf area was sensitive to the orientation of the lateral, its size (number of leaves, length, number of shoots), to the angle between the stem and the shoots, the distance between the leaves, and the angle between the leaf and the shoot. When the isolated lateral is placed in the context of a single tree, its photosynthesis per unit leaf area decreases by a factor of two due to the shadowing. For orchards, the photosynthesis per unit leaf area decreases as a function of the tree density. The photosynthesis per unit area of sunlit leaves at the lateral, tree or orchard levels, was found to be almost constant, because the leaf angle distribution of the illuminated leaves do not vary much. Therefore, one important result of this study is that the variation of photosynthesis per unit leaf area, is mainly determined by the fraction of sunlit leaf area.
Combining process-based and three-dimensional (3D) structural models for specific crops to functional–structural plant models (FSPMs) enable ecophysiologists to investigate the interaction of single plants or plant stands with their biotic and abiotic environment in a unique way. The present study was part of a collaborative research program on the development of a FSPM for the sample plant (Hordeum vulgare L.). The emphasis of this paper is put on two main aspects. First, improved generic and flexible functions are formulated for modeling the shape of leaves and stems of graminaceous plants as organ-related triangulated surfaces, where the parameters may be directly interpreted in terms of morphological traits. The proposed functions constitute the structural model, which is amplified by topological information to a so-called architectural model. Second, we suggest a new approach to parameterize these functions based on 3D point cloud data obtained by digitization of entire plants. Since no automated technique is available to process 3D point clouds in a way appropriate for parameterization of the architectural model, the required algorithms are developed and implemented in Matlab®. Our approach comprises the following steps. First, the measured set of points is partitioned into subsets representing each organ. Each subset is then divided further to represent organ segments. Next, the centroid of each partial point cloud representing an organ segment is computed. The sequence of these centroid points describes the organ axis. By means of the architectural model for leaves and stems, triangulated surfaces are assembled from the computed organ axis points and from user-defined initial values for the various parameters in the model (e.g. maximum leaf width). Finally, the parameters in the functions describing leaf and stem surfaces are estimated by fitting computed triangulated surfaces into the related point cloud using least squares optimization. Hence, the proposed method allows the use of 3D point clouds obtained with modern 3D digitizing techniques for the parameterization of an organ-based architectural model.
It is an ongoing challenge to develop and demonstrate management practices that increase the sustainability of agricultural systems. Soil carbon and nitrogen dynamics directly affect soil quality, crop productivity and environmental impacts. Root systems are central to the acquisition of water and nutrients by plants, but are also a major pathway for the inputs of carbon and nutrients to soil. The complexity of both biotic and abiotic interactions, combined with stochastic changes in root architecture, makes it difficult to understand below-ground dynamics on the basis of experimentation alone. The integration of dynamic models of above-ground growth, three-dimensional root system demography, and interactions between plants and the environment, into one single model is a major challenge because of the complexity of the systems.
During the past 10 years, soil scientists have started to use 3D Computed Tomography in order to gain a clearer understanding of the geometry of soil structure and its relationships with soil properties. We propose a geometric model for the 3D representation of pore space and a practical method for its computation. Our basic idea consists in representing pore space using a minimal set of maximal balls (Delaunay spheres) recovering the shape skeleton. In this representation, each ball could be considered as a maximal local cavity corresponding to the “intuitive” notion of a pore as described in the literature. The space segmentation induced by the network of balls (pores) was then used to spatialize biological dynamics. Organic matter and microbial decomposers were distributed within the balls (pores). A valuated graph representing the pore network, organic matter and distribution of micro-organisms was then defined. Microbial soil organic matter decomposition was simulated by updating this valuated graph. The method was implemented and tested using real CT images. The model produced realistic simulated results when compared with data in the literature in terms of the water retention curve and carbon mineralization. A decrease in water pressure decreased carbon mineralization, which is also in accordance with findings in the literature. From our results we showed that the influence of water pressure on decomposition is a function of organic matter distribution in the pore space. As far as we know, this is the approach to have linked pore space geometry and biological dynamics in a formal way. Our next goal will be to compare the model with experimental data of decomposition using different soil structures, and to define geometric typologies of pore space shape that can be attached to specific biological and dynamic properties.