Size-class Model Research Articles

Impact of Indian Ocean Dipole Events on Phytoplankton Size Classes Distribution in the Arabian Sea

Changes in the environmental condition associated with climatic events could potentially influence the PSC dynamics of the regional marine ecosystem. The Indian Ocean dipole (IOD) is one of the critical ocean–atmosphere interactions that affects the climate of the Arabian Sea, and it could be a potential factor influencing the regional PSC distribution. However, the relationship between PSC and IOD remains unclear and less explored. In this study, using the in-situ database acquired from the Arabian Sea, we reparametrized the three−component abundance−based phytoplankton size class model and applied it to reconstructed satellite−derived chlorophyll−a concentration to extract the fractional contribution of phytoplankton size classes to chlorophyll−a concentration. Further, we investigated the influence of IOD on the changes in the biological–physical properties in the Arabian Sea. The results showed that the biological–physical processes in the Arabian Sea are interlinked and the changes in the IOD mode control the physical variables like sea surface temperature (SST), sea surface height (SSH), and mixed layer depth (MLD), which influence the specific PSC abundance. Unprecedented changes in the PSC distribution and physical properties were observed during the extreme positive and negative IOD events, which clearly indicated the potential role of IOD in altering the PSC distribution in the Arabian Sea. This study highlights the impact of extreme climate events on PSC distribution and the need for a better understanding of the associated physical–biological–climate interactions.

A northeast United States Atlantis marine ecosystem model with ocean reanalysis and ocean color forcing

The northeast United States Atlantis model (NEUSv2) is an end-to-end ecosystem model that can simulate biogeochemical, ecological, fishery, management, and socio-economic processes within marine ecosystems. As a major update to the original model, NEUSv2 includes changes to the model's functional group definitions and forcing data. NEUSv2 is the first Atlantis model to use a satellite-ocean-color-derived phytoplankton size class model that was tuned specifically for the region to force marine primary production. Additionally, physical ocean variables (currents, temperature, and salinity) were updated using a high-resolution global ocean reanalysis. Despite its coarse resolution, NEUSv2 was capable of reproducing the broad spatial patterns seen in the physical and biological forcing sources, with the exception of some circulation features. NEUSv2 produced plausible zooplankton and planktivore biomass, a stable lower trophic food web, and recent trends in zooplankton biomass. NEUSv2 meets calibration criteria for the persistence and long-term stability of functional group biomass. Given the success of this new Atlantis forcing approach, we detail the observations and challenges regarding spatial scale-related processes, data assimilation, and biological calibration. We also discuss possible tradeoffs with model scope, calibration, and the availability of feedback mechanisms. This NEUSv2 hindcast is well suited for exploring ecosystem-level sensitivity to lower trophic processes and for testing alternative biogeochemical forcing. Further developments will improve model performance for higher trophic levels.

Diameter distribution model development of tropical hybrid Eucalyptus clonal plantations in Sumatera, Indonesia: A comparison of estimation methods

Background: Effective forest management and planning often requires information about the distribution of volume by size and product classes. Size-class models describe the diameter distribution and provide information by diameter class, such as the number of trees, basal area, and volume per unit of area. A successful diameter-distribution model requires high flexibility yet robust prediction of its parameters. To our knowledge, there are no studies regarding diameter distribution models for Eucalyptus hybrids in Indonesia. Therefore, the aim of this study was to compare different recovery methods for predicting parameters of the 3-parameter Weibull distribution for characterising diameter distributions of Eucalyptus hybrid clone plantations, on Sumatera Island of Indonesia. Methods: The parameter recovery approach was proposed to be compatible with stand-average growth and yield models developed based on the same data. Three approaches where compared: moment-based recovery, percentile-based prediction and hybrid methods. The ultimate goal was to recover Weibull parameters from future stand attributes, which were predicted from current stand attributes using regression models. Results: In this study, the moment method was found to give the overall lowest mean error-index and Kolmogorov– Smirnov (KS) statistic, followed by the hybrid and percentile methods. The moment-based method better fit long tails on both sides of the distribution and exhibited slightly greater flexibility in describing plots with larger variance than the other methods. Conclusions: The Weibull approach appeared relatively robust in determining diameter distributions of Eucalyptus hybrid clone plantation in Indonesia, yet some refinements may be necessary to characterize more complex distributions.

Length measurement accuracy of adaptive resolution imaging sonar and a predictive model to assess adult Atlantic salmon (Salmo salar) into two size categories with long-range data in a river.

Imaging sonars are used around the world for fish population monitoring. The accuracy of the length measurements has been reported in multiple studies for relatively short (<15 m) ranges and high image resolution. However, imaging sonars are often used at longer ranges (i.e., >15 m) where the images produced from sonar returns become less detailed. The accuracy of the length measurements from the Adaptive Resolution Imaging Sonar (ARIS) was tested by releasing n = 69 known-sized adult Atlantic salmon (Salmo salar) directly into the sonar field at ranges between 15 and 29 m, and measuring their echoes manually by four users and semi-automatically using a computer workflow in Echoview software. Overall, the length measurements were very variable: compared to true (fork) lengths, the mean of differences varied between -9.9 cm and 7.8 cm in the human-generated datasets, and between -42.8 cm and -20 cm in the computer-generated dataset. In addition, the length measurements in different datasets were only in poor or moderate agreement with each other (intraclass correlation <0.61). Contrary to our expectations, the distance from the transducer or the subjectively assessed echo quality did not have an effect on the measurement accuracy in most of the datasets and when it did, the effect was not systematic between the datasets. Therefore, a size class and length prediction model was implemented in a Bayesian framework to group salmon into two size categories: One-Sea-Winter (<63 cm) and Multi-Sea-Winter (≥63 cm) groups. The model correctly predicted the size category in 83% of the fish in the computer-generated dataset and ranged from 68% to 74% in the human-generated datasets. We conclude that fish length measurements derived from long-range imaging sonar data should be used with caution, but post-processing can improve the usefulness of the data for specific purposes, such as adult Atlantic salmon population monitoring.

Microstructure and mechanical response of TiB2/Al–Zn–Mg–Cu composites with more addition of Zn

In the present work, the in-situ TiB2/Al–Zn–Mg–Cu composites with different content of Zn element were fabricated by powder metallurgy. The interphases composed of solute elements are preferentially nucleated at the facets of the TiB2 particles during heating and the clusters in local region hinder the dissolution of the interphases into the matrix. Additionally, the phenomena of accelerated age-hardness response and reduced work hardening rate with increasing Zn content are found experimentally for TiB2/Al–Zn–Mg–Cu composites fabricated by powder metallurgy. A physically-based numerical model is developed to predict the precipitation kinetics and hardness as a function of aging time. The advanced reach to peak hardness results from accelerated nucleate rate and growth rate due to more addition of solute content. The influence of Zn content on the work hardening rate is discussed based on the state-parameters including solute content in the matrix, precipitate size and volume fraction. The effect of TiB2 particles on the parameters of the size class model is discussed qualitatively based on the experimental observations.

Phytoplankton Size Structure in Association with Mesoscale Eddies off Central-Southern Chile: The Satellite Application of a Phytoplankton Size-Class Model

Understanding the influence of mesoscale and submesoscale features on the structure of phytoplankton is a key aspect in the assessment of their influence on marine biogeochemical cycling and cross-shore exchanges of plankton in Eastern Boundary Current Systems (EBCS). In this study, the spatio-temporal evolution of phytoplankton size classes (PSC) in surface waters associated with mesoscale eddies in the EBCS off central-southern Chile was analyzed. Chlorophyll-a (Chl-a) size-fractionated filtration (SFF) data from in situ samplings in coastal and coastal transition waters were used to tune a three-component (micro-, nano-, and pico-phytoplankton) model, which was then applied to total Chl-a satellite data (ESA OC-CCI product) in order to retrieve the Chl-a concentration of each PSC. A sea surface, height-based eddy-tracking algorithm was used to identify and track one cyclonic (sC) and three anticyclonic (ssAC1, ssAC2, sAC) mesoscale eddies between January 2014 and October 2015. Satellite estimates of PSC and in situ SFF Chl-a data were highly correlated (0.64 &lt; r &lt; 0.87), although uncertainty values for the microplankton fraction were moderate to high (50 to 100% depending on the metric used). The largest changes in size structure took place during the early life of eddies (~2 months), and no major differences in PSC between eddy center and periphery were found. The contribution of the microplankton fraction was ~50% (~30%) in sC and ssAC1 (ssAC2 and sAC) eddies when they were located close to the coast, while nanoplankton was dominant (~60–70%) and picoplankton almost constant (&lt;20%) throughout the lifetime of eddies. These results suggest that the three-component model, which has been mostly applied in oceanic waters, is also applicable to highly productive coastal upwelling systems. Additionally, the PSC changes within mesoscale eddies obtained by this satellite approach are in agreement with results on phytoplankton size distribution in mesoscale and submesoscale features in this region, and are most likely triggered by variations in nutrient concentrations and/or ratios during the eddies’ lifetimes.

Modeling precipitate evolution in zirconium alloys during irradiation

The second phase precipitates (SPPs) in zirconium alloys are critical in controlling their performance. During service, SPPs are subject to both thermal and irradiation effects that influence volume fraction, number, and size. In this paper, a model has been developed to capture the combined effect of thermal and irradiation exposure on the Zr(Fe,Cr)2 precipitates in Zircaloy. The model includes irradiation induced precipitate destabilization integrated into a classical size class model for nucleation, growth and coarsening. The model has been applied to predict the effect of temperature and irradiation on SPP evolution. Increasing irradiation displacement rate is predicted to strongly enhance the loss of particles that arises from coarsening alone. The effect of temperature is complex due to competition between coarsening and irradiation damage. As temperature increases, coarsening is predicted to become increasingly important compared to irradiation induced dissolution and may increase resistance to irradiation induced dissolution by increasing particle size.

Growth strategies of an abundant reef-building coral in the southern Caribbean (Undaria tenuifolia)

The Caribbean coral community structure is changing, particularly species dominance. The Acropora die-off, at many southern and western Caribbean reefs, has allowed the branching scleractinian coral Undaria tenuifolia Dana to become one of the dominant species in these shallow reefs. In this study, colonies of U. tenuifolia were followed for a year using digital imagery. We developed a size class model that allowed us to estimate population fluctuation and growth strategies (colony growth rates and clonal processes). Growth rates varied with colony size; mid-sized colonies doubled their size in a year, while larger colonies often undergo fission. The clonal strategies, i.e., fusion and fission, allow U. tenuifolia to compensate for the low survival rates of early larval post-settlement thus being able to become a dominant coral species in the current reef scenario. The size matrix model formulated on the basis of a relative colony growth rate offered a practical approach for a short-term observation situation, generating a time- invariant transition model. Mortality patterns were not correlated to size classes and were variable by location and time. Overall, population abundance was stable over the monitored period of time. The high survivorship of U. tenuifolia after clonal fragmentation, as well as its high growth rate, suggests that this is a potential species to be cultured, which could be a solution given that this species has become the most common coral being extracted and sold as a souvenir in Cartagena, Colombia.

Linking individual-tree and whole-stand models for forest growth and yield prediction

Background: Different types of growth and yield models provide essential information for making informed decisions on how to manage forests. Whole-stand models often provide well-behaved outputs at the stand level, but lack information on stand structures. Detailed information from individual-tree models and size-class models typically suffers from accumulation of errors. The disaggregation method, in assuming that predictions from a whole-stand model are reliable, partitions these outputs to individual trees. On the other hand, the combination method seeks to improve stand-level predictions from both whole-stand and individual-tree models by combining them. Methods: Data from 100 plots randomly selected from the Southwide Seed Source Study of loblolly pine (Pinus taeda L.) were used to evaluate the unadjusted individual-tree model against the disaggregation and combination methods. Results: Compared to the whole-stand model, the combination method did not show improvements in predicting stand attributes in this study. The combination method also did not perform as well as the disaggregation method in tree-level predictions. The disaggregation method provided the best predictions of tree- and stand-level survival and growth. Conclusions: The disaggregation approach provides a link between individual-tree models and whole-stand models, and should be considered as a better alternative to the unadjusted tree model.

The assimilation of satellite‐derived data into a one‐dimensional lower trophic level marine ecosystem model

Lower trophic level marine ecosystem models are highly dependent on the parameter values given to key rate processes, however many of these are either unknown or difficult to measure. One solution to this problem is to apply data assimilation techniques that optimize key parameter values, however in many cases in situ ecosystem data are unavailable on the temporal and spatial scales of interest. Although multiple types of satellite-derived data are now available with high temporal and spatial resolution, the relative advantages of assimilating different satellite data types are not well known. Here these issues are examined by implementing a lower trophic level model in a one-dimensional data assimilative (variational adjoint) model testbed. A combination of experiments assimilating synthetic and actual satellite-derived data, including total chlorophyll, size-fractionated chlorophyll and particulate organic carbon (POC), reveal that this is an effective tool for improving simulated surface and subsurface distributions both for assimilated and unassimilated variables. Model-data misfits were lowest when parameters were optimized individually at specific sites; however, this resulted in unrealistic overtuned parameter values that deteriorated model skill at times and depths when data were not available for assimilation, highlighting the importance of assimilating data from multiple sites simultaneously. Finally, when chlorophyll data were assimilated without POC, POC simulations still improved, however the reverse was not true. For this two-phytoplankton size class model, optimal results were obtained when satellite-derived size-differentiated chlorophyll and POC were both assimilated simultaneously.

Modeling particle aggregation using size class and size spectrum approaches

[1] Particle aggregation plays an important role in many marine biogeochemical processes such as determining the vertical flux of particulate material and trace metal scavenging. Models of particle aggregation vary in complexity and in this paper I compare the behavior of a detailed size spectrum model with that of a series of simple, two size class models with different representations of aggregation, all of which have appeared in the literature. The simplest model uses a first-order representation of aggregation kinetics, while two other models have nonlinear representations of aggregation. The simplest model is unable to reproduce the dynamic or steady state behavior of the size spectrum model. Results from the two nonlinear size class models show better agreement with the behavior of the size spectrum model. I find that the mode of aggregation and the dependence of aggregation on particle size are crucial for understanding the differences between the models.

A tree distance-dependent growth and yield model for naturally regenerated pure uneven-aged maritime pine stands in central inland of Portugal

Maritime pine (Pinus pinaster Aiton) is one of the most important Portuguese species, growing in pure stands ranging from even-aged to multi-aged structures. Current growth and yield models were developed only for even-aged, managed stands and/or for very specific regions of Portugal. This paper focuses on the validation of the existing size-class model PBRAVO, adapted to even-aged stands, and on the subsequent development of a single tree distance-dependent growth and yield model (PBIRROL), both in distance-independent and distance-dependent versions, for uneven-aged stands. The new model is composed of four modules, each with a set of sub-models for: tree variable prediction, tree volume prediction, future tree list prediction and growth projection. The evaluation of the PBRAVO and PBIRROL models showed that the new model gives more accurate predictions. Moreover, medium-term simulations provided consistent and logical predictions. It was verified that individual tree models are more suited to simulate poorly managed uneven-aged stands than diameter distribution models. No clear superiority of distance-dependent models was found over models using just distance-independent measures of inter-tree competition.

Le modèle spatial de rendement est-il une solution plus pertinente pour l¿exploitation des forêts tropicales ?

L'expérience de sociétés forestières pratiquant l'abattage sélectif d'arbres individuels au Sarawak montre que leurs opérations ne sont encadrées en général que par une réglementation de base concernant par exemple le diamètre minimum d'abattage, les dimensions maximales des trouées ou les essences autorisées. En général, les aspects sylvicoles ne sont, au mieux, pris en compte qu'implicitement dans les règlements sur l'abattage, et non explicitement dans la planification et les opérations d'abattage. Les sociétés forestières tendent de plus en plus à pratiquer un comptage à 100 % des arbres, associé à une cartographie par GPS, dans le cadre de systèmes d'exploitation à impact faible ou réduit. L'utilisation de systèmes GPS permet d'élaborer des bases de données spatialisées et des descripteurs clés des arbres. Ceux-ci pourraient servir d'appui à la planification d'activités futures basées sur des modèles de croissance et de rendement permettant de prendre en compte des paramètres forestiers importants, comme le DBH et les essences présentes, et de cartographier la position des arbres. Cela aiderait les gestionnaires des récoltes et les arpenteurs forestiers à passer d'une optique purement opérationnelle à une optique sylvicole, en prenant en compte des paramètres tels que l'espacement, le recrutement et les tiges d'avenir lors de la planification des opérations d'abattage. L'absence de ce type de planification parmi les sociétés forestières est principalement due au fait que les modèles d'exploitation forestière en milieu tropical sont généralement basés sur des paramètres de zonage (modèles à l'échelle des peuplements ou selon les classes de dimension) qui ne sont pertinents que par rapport aux besoins de la planification à grande échelle. Ces modèles n'intègrent généralement pas les données spatiales générées par la planification, ils nécessitent des données beaucoup plus nombreuses et différentes de celles utilisées pour planifier les opérations d'abattage et ne produisent pas de résultats utiles à la planification opérationnelle des récoltes. Il est plus utile de se servir de modèles par arbre qui intègrent des données spatiales et produisent des résultats pertinents au plan opérationnel, comme les diamètres, les essences et la position des arbres. Cet article rappelle les contraintes opérationnelles de l'exploitation forestière à impact faible ou réduit, et préconise des approches pour la modélisation des rendements compatibles avec ces systèmes.

Analysis and management of stand dynamics of Vietnamese dipterocarp forests by applying a dynamic growth model

The dipterocarp forests in the Central Highland of Vietnam are threatened by overharvesting. In addition, wildfires frequently affect their dynamics. Sustainable management of this unique forest type is of important concern. This study aims at providing a first set of operational information for forest management with a model-based approach. Specifically, we (a) evaluate selected cutting regimes with focus on maximum sustainable yield, (b) explore transformation times from a given to a desired forest state, and (c) preliminarily assess wildfire effects on yield. A size class model was developed as a tool to address these issues. Various diameter distributions defined by the q factor concept were used as possible desired equilibrium states to be assessed. Maximum yields were estimated between 3.9 and 2.7 m3 ha−1 year−1, depending on site quality. Based on data from overharvested stands, time for reaching desired equilibria ranged between 20 and 60 years. In stands with frequent severe wildfires, the long-term yield may decrease by 40%. Our results suggest the model being an effective tool for simulating effects of treatment alternatives. We conclude that, despite a poor information basis, it is necessary to develop and refine such models for supporting sustainable forest management in Vietnam.

A multivariate analysis of biophysical factors and forest fires in Spain, 1991 - 2005

The main goal of this study was to explain the relationship between forest fires and different climatic, topographic and vegetation factors, establishing explanatory models from multivariate analysis. The study area comprised peninsular Spain. Two dependent variables were considered: probability of burning and fire size class, from a forest-fire map derived from visual analysis of satellite images from 1991 to 2005 (3337 fires greater than 25 ha). Logistic regression, discriminant analysis and regression trees were used to analyse the probability of burning. The models showed a significant relationship with land cover and slope, where the classification achieved an agreement of ~66%, and this was very similar for the three statistical methods used. Discriminant analysis and regression trees were used to model fire size class. These models appeared more related to ecozones and climatic variables (winter precipitation and mean summer temperature). In this case, the best classification results were obtained in the category of very large fires (&gt;5000 ha), with an agreement above 80%. Regression trees achieved better results for fire size class models.

Model of phytoplankton absorption based on three size classes

Using the phytoplankton size-class model of Brewin et al. [Ecol. Model.221, 1472 (2010)], the two-population absorption model of Sathyendranath et al. [Int. J. Remote. Sens.22, 249 (2001)] and Devred et al. [J. Geophys. Res.111, C03011 (2006)] is extended to three populations of phytoplankton, namely, picophytoplankton, nanophytoplankton, and microphytoplankton. The new model infers total and size-dependent phytoplankton absorption as a function of the total chlorophyll-a concentration. A main characteristic of the model is that all the parameters that describe it have biological or optical interpretation. The three-population model performs better than the two-population model at retrieving total phytoplankton absorption. Accounting for the contributions of picophytoplankton and nanophytoplankton, rather than the combination of both as in the two-population model, improved significantly the retrieval of phytoplankton absorption at low chlorophyll-a concentrations. Class-dependent specific absorption of phytoplankton derived using the model compares well with previously published models. However, the model presented in this paper provides the specific absorption of three size classes and is applicable to a continuum of chlorophyll-a concentrations. Absorption obtained from remotely sensed chlorophyll-a using our model compares well with in situ absorption measurements.

Particle Characterization and Settling Velocities for a Water Supply Reservoir during a Turbidity Event

Characterization of the particle population for a location in a water supply reservoir, Kensico Reservoir, N.Y., is documented for a high turbidity event, from its onset, through alum treatment and its waning. Supporting in situ measurements included the beam attenuation coefficient at 670nm(c670) and 660nm(c660) [surrogates of turbidity (Tn)], particle concentrations (N) and size distributions (PSDs), and size class specific settling velocities (SVs). Laboratory measurements included chemical and morphometric analyses of individual particles, and routine measurements of Tn. The turbidity is shown to be primarily derived from clay minerals, mostly in the size range of 1.5–6μm. An initial high c670 level (40m−1;Tn∼100NTU) decreased sevenfold in less than 1week in response to alum treatment that largely eliminated the particle size classes responsible for the elevated turbidity. Successful SV experiments, made using a laser in situ scattering and transmissometry (LISST) instrument, for seven particle size classes in the range of 1.25–129μm yielded SV values of 0.17–69.4mday−1. Size classes larger than ∼5μm settled much slower than Stokes law predictions, before alum treatment, indicating that these classes existed as porous flocs or aggregates. Decreases in SVs following treatment suggest changes in floc character consistent with increased porosity. In situ measurements of c670, N, PSDs, and SVs can contribute to the development and testing of a multiple particle size class model to simulate fate, transport, and impacts of suspended particles.

Ecosystem model complexity versus physical forcing: Quantification of their relative impact with assimilated Arabian Sea data

In the wake of recent large-scale observational programs such as Joint Global Ocean Flux Study (JGOFS) and Global Ocean Ecosystems Dynamics (GLOBEC), a number of models have been developed to simulate biogeochemical cycling in various oceanographic regions; however, few quantitative comparisons of these models have been made. In order to assess critically which ecosystem structures and model formulations are best able to simulate observed biogeochemical cycling, three fundamentally different ecosystem models of varying complexity are applied within a consistent one-dimensional (1-D) framework at 15.5°N, 61.5°E in the Arabian Sea. Each model is forced by three different sets of physical forcing fields: two of these are derived from the solutions of different 3-D physical models, while the third is derived from moored observations. In situ concentrations of plankton and nitrogenous nutrients, and rates of production and export measured during the US JGOFS Arabian Sea Expedition are used for assimilation and evaluation. After objectively optimizing each model, their performance is quantitatively compared to assess which model structure best represents the fundamental underlying biogeochemical processes. Results are highly sensitive to the number of parameters optimized for each ecosystem model. A set of cross-validation experiments designed to assess predictive capability demonstrates that when too many parameters are allowed to vary, the more complex models are unable to reproduce any unassimilated data, suggesting that under these conditions the models have little predictive skill. Optimizing only an objectively and systematically selected subset of uncorrelated model parameters minimizes this problem. The method developed to accomplish this parameter selection is presented. After this optimization method is applied, all three models behave similarly, implying that the additional complexity of a multiple size-class model may not be advantageous. Furthermore, a change in physical model (mixed-layer depth and vertical velocity fields) typically produces a far greater change in biogeochemical model response than does a change in ecosystem model complexity, highlighting the fact that biogeochemical variability is largely determined by the physical environment both in situ and in the model domain.

Density-dependent predation ofChaoborus flavicans onDaphnia longispina in a small lake: the effect of prey size

Functional response curves of fourth instar larvae ofChaoborus flavicans preying on two size classes ofDaphnia longispina were examined throughout three summer seasons in a small forest lake. Data for each size class were fitted to Holling's disc equation. The parametersa′ (attack rate) andTh (handling time) were calculated for each prey size from these curves. Attack rate was greater and handling time was shorter for small (0.77 mm) than for large (1.82 mm)Daphnia. In 1:1 mixture of these prey size classes the predation rates ofChaoborus on smallDaphnia at prey densities above 20 l−1 were greater than predicted from the single size-class experiments. The observed predation rates on largeDaphnia were lower than predicted at all prey densities. Since both single size-class and two size-class experiments were run during the same period of time the difference in observed and predicted predation rates could not be attributed to seasonal changes in prey preference ofChaoborus larvae. In experiments with a concentrated mixture of lake zooplankton (dominated byD. longispina)Chaoborus preference forDaphnia decreased as prey body size increased. There was no obvious correlation between selectivity coefficients and size-frequency distributions ofDaphnia. When medium-sizedDaphnia were omitted from calculations the preference of small over large prey did not differ significantly from the predictions of the single size-class model.