Ecophysiological Model Research Articles

An ecophysiological model of plant-pest interactions: the role of nutrient and water availability.

Empirical studies have shown that particular irrigation/fertilization regimes can reduce pest populations in agroecosystems. This appears to promise that the ecological concept of bottom-up control can be applied to pest management. However, a conceptual framework is necessary to develop a mechanistic basis for empirical evidence. Here, we couple a mechanistic plant growth model with a pest population model. We demonstrate its utility by applying it to the peach-green aphid system. Aphids are herbivores which feed on the plant phloem, deplete plants' resources and (potentially) transmit viral diseases. The model reproduces system properties observed in field studies and shows under which conditions the diametrically opposed plant vigour and plant stress hypotheses find support. We show that the effect of fertilization/irrigation on the pest population cannot be simply reduced as positive or negative. In fact, the magnitude and direction of any effect depend on the precise level of fertilization/irrigation and on the date of observation. We show that a new synthesis of experimental data can emerge by embedding a mechanistic plant growth model, widely studied in agronomy, in a consumer-resource modelling framework, widely studied in ecology. The future challenge is to use this insight to inform practical decision making by farmers and growers.

No evidence for carryover effect in tree rings based on a pulse-labelling experiment on Juniperus communis in South Germany

A clear carryover effect of tree-ring formation was not detected based on a pulse-labelling experiment conducted on Juniperus communis in South Germany. The inherent linkage between photosynthesis and the formation of wood is important for the understanding of relationships between tree-ring series and climate/environmental data in dendroclimatology studies. However, it is impossible to reach a mechanistic procedure of tree stem radial growth depending on its carbon balance from a traditional statistical point of view alone. Pulse labelling experiment with stable carbon isotope (13CO2) has provided innovative insights into the fate of recently assimilated carbon in organs and carbon-containing compounds. In this study, we conducted an in situ pulse labelling experiment on 27 July 2016 to examine the response of tree ring and different-aged needles to short-term elevated 13CO2 of a juniper shrub growing on a heathland in South Germany. New and old needles from four expositions were sampled before and after the experiment. A wood segment was taken from the main branch and stable carbon isotope composition (δ13C) was analysed at an intra-annual time scale. Before the experiment, the mean δ13C was − 26.8 ± 0.4‰ (mean ± standard deviation) for both needle ages, while woody tissue showed about 3‰ higher δ13C compared to needles. Substantial enriched 13C was detected in the needles after the experiment. New needles showed significant higher δ13C than the old ones 1–7 days after the experiment. Significant enriched δ13C was detected in the wood from 35 to 61% of the annual tree ring in 2016, indicating that the short-term enrichment of 13C can affect wood formation for a large section. No enhancement in 13C signal appeared in the tree ring of the subsequent year 2017, suggesting the absence of a carryover effect. Wood formation did not reply on the carbohydrates stored even 1 year before and thus tree-ring ecophysiological modelling as well as dendrochronological studies should therefore benefit from such result.

Optimal maternal incubation strategies for altricial and precocial birds

Parental management of early offspring development is a complex but essential contribution to reproductive success for oviparous species. Mothers faced with incubating eggs in a variable environment maximize reproductive success by adaptively adjusting offspring number in a clutch and incubation schedules to facilitate egg development while meeting their own energetic demands. However, the optimal resolution of these trade-offs varies with physiological and ecological factors and is thus hard to determine using empirical data from natural systems. To untangle the complex factors driving optimal maternal decisions, we developed an ecophysiological model that identifies optimal maternal strategies and their implications for offspring, comparing results for example altricial and precocial species. We showed how plastic maternal strategies can ameliorate effects of food scarcity, offspring mortality risk, variable levels of biparental care, and constraints on producing multiple clutches within a season. In the altricial case in particular, we identified important downstream fitness effects from decisions made in the incubation stage. Additionally, we showed that seasonal decreases in clutch size may result from cumulative fecundity costs for the altricial case and from a seasonal decline in female quality for precocial species. Many of the novel results in this study are testable in future empirical work.

Integrating evolutionary, demographic and ecophysiological processes to predict the adaptive dynamics of forest tree populations under global change

Three types of process-based models (PBMs) are traditionally used to predict the response of forest tree populations to global change (GC): (i) ecophysiological models, which simulate carbon and water fluxes in forest ecosystems by explicitly integrating the effects of climate and CO2; (ii) forest dynamics models which simulate forest successions by explicitly linking mortality, growth and regeneration processes; and (iii) evolutionary dynamics models, which simulate the variation and evolution of adaptive traits by explicitly accounting for selection, mutation, gene flow and inheritance rules. The ongoing context of rapid GC, however, questions the boundaries between these types of models. Here, we review different strategies of model integration: (i) physio-demographic PBMs, integrating physiological and demographic processes; (ii) demo-genetic PBMs, integrating demographic and evolutionary processes; and (iii) physio-demo-genetic PBMs, which attempt to integrate these three types of processes. We show that these integrative models allow a better understanding of how different functional traits influence demographic rates (the phenotype-demography map), how the variation in demographic rates influences fitness (the demography-fitness map) and how individual variations of fitness may in turn influence the genetic composition of a population. Our review highlights that accounting for inter-individual variation in ecological processes is increasingly recognized as crucial for modelling the ecosystem response to environmental change. We argue that the effort of integrating these different processes is valuable, both for a basic understanding of their interactive effects on the responses of forests to GC and for applied horizon scanning to support adaptive strategies.

Probability of Spring Frosts, Not Growing Degree-Days, Drives Onset of Spruce Bud Burst in Plantations at the Boreal-Temperate Forest Ecotone.

Climate warming-driven early leaf-out is expected to increase forest productivity but concurrently increases leaf exposure to spring frosts, which could reduce forests' net productivity. We hypothesized that due to their damaging effect on buds, spring frosts exert a stronger control on bud phenology than do growing degree-days. We monitored bud flush phenology of three white spruce seed sources (one local seed source from the boreal mixedwood forest and two seed sources from the temperate forest), one black spruce seed source originating from the boreal mixedwood forest and four nonlocal Norway spruce seed sources in 2016 and 2017 in two plantations located on both sides of the temperate-boreal mixedwood forest ecotone in eastern Canada (Quebec). We aimed to determine inter- and intraspecies variations in bud break timing and sensitivity to air temperature and photoperiod. We expected that bud break timing for boreal species and seed sources would be better synchronized with the decrease in frost probability than for nonlocal species and seed sources. We used mixed binomial regressions and AICc model selection to determine the best environmental variables predicting each transition from one stage of bud phenology to the next. At both plantation sites, white spruce bud flush began and ended earlier compared to black and Norway spruce. Buds of all spruce species were sensitive to frost probability for early phenological stages, whereas growing degree-days controlled the remaining stages. Photoperiod sensitivity was higher for white spruce compared to black and Norway spruce and reached its maximum in the temperate forest. At intraspecies level, the two southern white spruce seed sources opened their buds earlier than the local source and were more sensitive to photoperiod, which increased their exposure to spring frosts. Onset of spruce bud flush is driven by spring frosts and photoperiod, but once started, bud phenology responds to temperature. The high photoperiod sensitivity in white spruces could counterbalance climate warming and limit future premature leaf-out, whereas the low photoperiod sensitivity in black spruce should not restrain leaf-out advancement with climate warming. Our results call for adapting the temperature-driven hypotheses of ecophysiological models predicting leaf-out to include spring frost probability.

V-Mango: a functional-structural model of mango tree growth, development and fruit production.

Background and AimsMango (Mangifera indica L.) is the fifth most widely produced fruit in the world. Its cultivation, mainly in tropical and sub-tropical regions, raises a number of issues such as the irregular fruit production across years, phenological asynchronisms that lead to long periods of pest and disease susceptibility, and the heterogeneity of fruit quality and maturity at harvest. To address these issues, we developed an integrative functional–structural plant model that synthesizes knowledge about the vegetative and reproductive development of the mango tree and opens up the possible simulation of cultivation practices.MethodsWe designed a model of architectural development in order to precisely characterize the intricate developmental processes of the mango tree. The appearance of botanical entities was decomposed into elementary stochastic events describing occurrence, intensity and timing of development. These events were determined by structural (position and fate of botanical entities) and temporal (appearance dates) factors. Daily growth and development of growth units and inflorescences were modelled using empirical distributions and thermal time. Fruit growth was determined using an ecophysiological model that simulated carbon- and water-related processes at the fruiting branch scale.Key ResultsThe model simulates the dynamics of the population of growth units, inflorescences and fruits at the tree scale during a growing cycle. Modelling the effects of structural and temporal factors makes it possible to simulate satisfactorily the complex interplays between vegetative and reproductive development. The model allowed the characterization of the susceptibility of mango tree to pests and the investigatation of the influence of tree architecture on fruit growth.ConclusionsThis integrative functional–structural model simulates mango tree vegetative and reproductive development over successive growing cycles, allowing a precise characterization of tree phenology and fruit growth and production. The next step is to integrate the effects of cultivation practices, such as pruning, into the model.

Determination of the most effective design for the measurement of photosynthetic light-response curves for planted Larix olgensis trees

A photosynthetic light-response (PLR) curve is a mathematical description of a single biochemical process and has been widely applied in many eco-physiological models. To date, many PLR measurement designs have been suggested, although their differences have rarely been explored, and the most effective design has not been determined. In this study, we measured three types of PLR curves (High, Middle and Low) from planted Larix olgensis trees by setting 31 photosynthetically active radiation (PAR) gradients. More than 530 million designs with different combinations of PAR gradients from 5 to 30 measured points were conducted to fit each of the three types of PLR curves. The influence of different PLR measurement designs on the goodness of fit of the PLR curves and the accuracy of the estimated photosynthetic indicators were analysed, and the optimal design was determined. The results showed that the measurement designs with fewer PAR gradients generally resulted in worse predicted accuracy for the photosynthetic indicators. However, the accuracy increased and remained stable when more than ten measurement points were used for the PAR gradients. The mean percent error (M%E) of the estimated maximum net photosynthetic rate (Pmax) and dark respiratory rate (Rd) for the designs with less than ten measurement points were, on average, 16.4 times and 20.1 times greater than those for the designs with more than ten measurement points. For a single tree, a unique PLR curve design generally reduced the accuracy of the predicted photosynthetic indicators. Thus, three optimal measurement designs were provided for the three PLR curve types, in which the root mean square error (RMSE) values reduced by an average of 8.3% and the coefficient of determination (R2) values increased by 0.3%. The optimal design for the High PLR curve type should shift more towards high-intensity PAR values, which is in contrast to the optimal design for the Low PLR curve type, which should shift more towards low-intensity PAR values.

Application and evaluation of the dendroclimatic process-based model MAIDEN during the last century in Canada and Europe

Abstract. Tree-ring archives are one of the main sources of information to reconstruct climate variations over the last millennium with annual resolution. The links between tree-ring proxies and climate have usually been estimated using statistical approaches, assuming linear and stationary relationships. Both assumptions may be inadequate, but this issue can be overcome by ecophysiological modelling based on mechanistic understanding. In this respect, the model MAIDEN (Modeling and Analysis In DENdroecology) simulating tree-ring growth from daily temperature and precipitation, considering carbon assimilation and allocation in forest stands, may constitute a valuable tool. However, the lack of local meteorological data and the limited characterization of tree species traits can complicate the calibration and validation of such a complex model, which may hamper palaeoclimate applications. The goal of this study is to test the applicability of the MAIDEN model in a palaeoclimate context using as a test case tree-ring observations covering the 20th century from 21 Eastern Canadian taiga sites and 3 European sites. More specifically, we investigate the model sensitivity to parameter calibration and to the quality of climatic inputs, and we evaluate the model performance using a validation procedure. We also examine the added value of using MAIDEN in palaeoclimate applications compared to a simpler tree-growth model, i.e. VS-Lite. A Bayesian calibration of the most sensitive model parameters provides good results at most of the selected sites with high correlations between simulated and observed tree growth. Although MAIDEN is found to be sensitive to the quality of the climatic inputs, simple bias correction and downscaling techniques of these data improve significantly the performance of the model. The split-sample validation of MAIDEN gives encouraging results but requires long tree ring and meteorological series to give robust results. We also highlight a risk of overfitting in the calibration of model parameters that increases with short series. Finally, MAIDEN has shown higher calibration and validation correlations in most cases compared to VS-Lite. Nevertheless, this latter model turns out to be more stable over calibration and validation periods. Our results provide a protocol for the application of MAIDEN to potentially any site with tree-ring width data in the extratropical region.

A spatial model for nutrient mitigation potential of blue mussel farms in the western Baltic Sea

Worldwide, coastal and marine policies are increasingly aiming for environmental protection, and eutrophication is a global challenge, particularly impairing near-coastal marine water bodies. In this context, mussel mitigation aquaculture is currently considered an effective tool to extract nutrients from such water bodies. Mussel mitigation farming using longline systems with loops of collector material is a well-developed technology and considered promising in the western Baltic Sea. Besides several spatially limited field studies, a suitable spatial model for site-specific implementation is still lacking. In this study, we present a modular spatial model, consisting of a spatial and temporal habitat factor model (Module 1), blue mussel growth model (Module 2), mussel farm model (Module 3), and an avoidance of food limitation model (Module 4). The modules integrate data from in situ monitoring, mussel growth experiments, and eco-physiological modelling for the western Baltic Sea, to estimate spatially explicit nutrient reduction potentials. The model is flexible with respect to farm setups and harvest times and considers natural variability, model uncertainty, and required hydrodynamics. Modelling results proved valid at all scales and modules, and point out key areas for efficient mussel mitigation farms in Danish, German and Swedish areas. Modelled long-term mean mitigation potentials for harvest in November reach up to 0.88 tN/ha and 0.05 tP/ha for a farm setup using 2 m depth-range of the water column and 3.0 tN/ha and 0.17 tP/ha using up to 8 m, respectively. For Danish water bodies, we demonstrate that in efficient areas, mitigation farms (18.8 ha, 90 km collector substrate in loops with 2 m depth-range) required <3.6% of the space to extract the target nitrogen loads for good ecological status. The developed approach could prove valuable for implementing environmental policies in aquatic systems, e.g. in situ nutrient mitigation, aquaculture spatial planning, and habitat suitability mapping.

Probabilistic modeling to estimate jellyfish ecophysiological properties and size distributions

While Ocean modeling has made significant advances over the last decade, its complex biological component is still oversimplified. In particular, modeling organisms in the ocean system must integrate parameters to fit both physiological and ecological behaviors that are together very difficult to determine. Such difficulty occurs for modeling Pelagia noctiluca. This jellyfish has a high abundance in the Mediterranean Sea and could contribute to several biogeochemical processes. However, gelatinous zooplanktons remain poorly represented in biogeochemical models because uncertainties about their ecophysiology limit our understanding of their potential role and impact. To overcome this issue, we propose, for the first time, the use of the Statistical Model Checking Engine (SMCE), a probability-based computational framework that considers a set of parameters as a whole. Contrary to standard parameter inference techniques, SMCE identifies sets of parameters that fit both laboratory-culturing observations and in situ patterns while considering uncertainties. Doing so, we estimated the best parameter sets of the ecophysiological model that represents the jellyfish growth and degrowth in laboratory conditions as well as its size. Behind this application, SMCE remains a computational framework that supports the projection of a model with uncertainties in broader contexts such as biogeochemical processes to drive future studies.

Evaluating short-season soybean management adaptations for cover crop rotations with a crop simulation model

Cover crop fall biomass production and thus successful provisioning of ecosystem services depend on the previous cash crop harvest date. We used a process-based eco-physiological model to investigate the potential of short-season soybean maturity groups (MG) to lengthen the cover crop growing window while achieving yields similar to full-season MG cultivars. Cultivar coefficients for MG 0–4 cultivars for the DSSAT – CROPGRO model were calibrated with data from 13 site-years (in 2017 and 2018) across Kentucky, Nebraska, and Ohio. The model was efficient in predicting differences in soybean harvest maturity date (R8; Model efficiency [ME] = 0.61; Root Mean Square Error [RMSE] = 7.4 days) and yield (ME = 0.38; RMSE = 0.452 Mg ha−1) for an independent set of soybean cultivars in the same site-years. Thereafter, a multi-factor sensitivity analysis across 30-yr of historical weather data was conducted. Simulated results showed that MG 3 cultivars would not reduce yield and would advance cover crop establishment compared to MG 4 cultivars. For planting dates in May and conditions of no water stress, adaptating cultivar choices to MG lower than 3 would reduce yields by 55 to 567 kg ha−1 per unit decrease in MG. Under water stress or when planting date was delayed, adapting cultivar choices to MG lower than 3 had a less detrimental effect on yield. Overall, switching to earlier cutlivar maturities would advance soybean harvest by 7–11 days MG−1 (May 15 planting date) or 1–7 days MG-1 (Jul 1 planting date), and lengthen the cover crop growing season in the fall by 95–198 °C day MG−1 (May 15 planting date) or 19−104 °C day MG−1 (Jul 1 planting date). The greater potential to increase the cover crop growing season with short-season MG cultivars was also associated with a greater soybean yield penalty in the warmest locations in our study. Using crop coefficients calibrated by MG rather than by specific cultivar provided a way to increase model application within a study region to study cultivar maturity adaptations for crop rotations while reducing the need for calibration. Further studies that analyze the tradeoffs from soybean cultivar adaptation on C, N, and water balance, and other indirect ecosystem services from cover crops are necessary.

Irrigated rice sowing periods based on simulated grain yield

This study aimed to establish the sowing periods with lower climatic risk for irrigated rice cultivation in the State of Rio Grande do Sul, Brazil, using the grain yield simulated by the SimulArroz which is an eco-physiological model based in processes that also simulates the development and the growth of the crop. The simulated grain yields were normalized for each of the four groups of cultivars, referenced as Relative Yield (YRel), considering the values of Reference Yield (YRef). The YRef considered was equal to the 80 percentile for the cultivars of groups 1 and 2 (Very short and Short cycle, respectively), and to the 90 percentile, for the cultivars of groups 3 and 4 (Medium and Long cycle, respectively). A successful year was when the YRel value was equal to or greater than 60% of YRef. These years were used to classify the climatic risk levels of 20% (80% success), 30% (70% success) and 40% (60% success). The preferential sowing periods for the four cultivar groups, at three levels of climatic risk, were established for all counties of the State of Rio Grande do Sul. The results indicated that the beginning of sowing occurs, depending on the group of cultivars, on September 1st or September 11th. On the other hand, the final sowing date varies with the group of cultivars and the level of risk involved, being December 20th the last sowing date for group 1 (Very short cycle). It is concluded that the SimulArroz model is appropriate to define the sowing periods of irrigated rice in Rio Grande do Sul considering grain yield, cycle of the cultivars and level of climatic risk.

Multisite evaluation of the 3-PG model for the highest phenotypic plasticity Eucalyptus clone in Brazil

Different ecophysiological models are used to predict the productivity of forest plantations worldwide. The Physiological Principles in Prediction Growth (3-PG) model has been successfully used for this purpose since 1997. In this study, the 3-PG model was parameterized and validated to predict the productivity of the most planted clonal eucalypts in Brazil (Eucalyptus urophylla) in different regions of the country and assess the attainable productivity of this same clone for a region little exploited for this plantations in the country. Through data collection carried out between 2012 and 2018, conducted in 36 sites distributed across an environmental gradient that spans over 3500 km in Brazil, it was possible to parameterize the 3-PG model using data that represent a portion of the soil and climate diversity of forest plantations in Brazil. We determined the initial biomass of the compartments (stem, leaf, and root), relationship between net and gross productivity, average wood density, maximum stem biomass for 1000 trees ha−1, maximum stomatal conductance, bark and branch fractions for the initial and mature age, specific leaf area for the initial and mature age, and allometric parameters at different ages. Considering the model initialization period, the initial age was defined at 12 months after planting and the final age was considered at 80 months. Model calibration was performed in four experimental sites (special sites for the calibration set), which correspond to the environmental diversities of the project, and model validation was performed by applying the setup of the model obtained in 10 other sites (regular sites for validation set) of the same experimental network. In all sites tested, estimates of basal area, diameter at breast height, leaf area index, and stem biomass agreed with the measured values. On average, the estimates of diameter and breast height were 4.91% higher than the observed measures, whereas the stem biomass estimates were 16.44% lower and the leaf area index was 26.22% lower; moreover, the estimates were overestimated for the first three years and underestimated in recent years. Overall, the model was able to capture the soil and climate differences for predicting E. urophylla clone productivity. Its application in a region not yet exploited for eucalypts plantations may help investors in the region select areas for acquisition, planting extensions, and extend production technologies.

Activity niches outperform thermal physiological limits in predicting global ant distributions

AbstractAimThermal physiology is commonly used in mechanistic models to predict species distribution and project distribution change. Such thermal constraints for ants are often measured under laboratory conditions as critical thermal limits (CTmax and CTmin), but have also been observed in the field as foraging thermal limits (FTmin and FTmax). Here we compared distribution projections based on ant physiological and behavioural thermal limits with their realized distributions to assess the validity of using ecophysiological models to predict species ranges over large scales.LocationGlobal.TaxonAnts.MethodsFrom published literature, we compiled a database of 148 lab‐measured critical thermal limits and 137 field‐observed foraging thermal limits for 20 ant genera distributed around the world. We projected their potential ranges using active hour thresholds by matching their thermal limits or preferred thermal breadth (incorporating thermal optima) with hourly surface temperature data. We then compared the projections against a comprehensive global database of ant distributions to assess the validity of physiologically and behaviourally based models.ResultsWe found that surface temperature plays a clear role in generic‐level ant biogeography. Projections based on foraging thermal limits were more conservative in constraining ranges to observed distributions, while projections based on critical thermal limits often overestimated ranges. Furthermore, thermal limit models can be improved by incorporating behaviourally derived thermal optima.Main conclusionsOur results suggest that temperature‐dependent activity niches and thermal optima better reveal species realized thermal niches than critical thermal limits, and hence can improve predictions of species distributions and future distribution changes under climate change.

Biological, socio-economic, and administrative opportunities and challenges to moving aquaculture offshore for small French oyster-farming companies

Oyster production has historically taken place in intertidal zones, and shellfish farms already occupy large extents of the French intertidal space. The expansion of French shellfish aquaculture within intertidal areas is therefore spatially limited, and moving production to the subtidal offshore environment is considered to be a possible solution to this problem. Finding new sites along the French Atlantic coast was studied here from the perspective of small oyster companies run by young farmers, who are interested in offshore bivalve aquaculture expansion compatible with their investment capacity. In assessing the feasibility of such offshore production, we considered three main issues: (1) bivalve growth potential and (2) technical feasibility and conflicting uses, both within a spatial framework, as well as (3) the steps and barriers of the administrative licensing process. Oyster spat in an experimental offshore cage showed significantly faster growth, in terms of both weight and length, compared to those in an intertidal cage, mainly due to lower turbidity and full-time feeding capacity (i.e., constant immersion in the water). A combination of Earth Observation data and bivalve ecophysiological modelling was then used to obtain spatial distribution maps of growth potential, which confirmed that offshore sites have better potential for oyster growth than the traditionally oyster-farmed intertidal sites overall, but that this is highly spatially variable. Small-scale producers indicated two technical factors constraining where farms could be located: bathymetry must be between 5 and 20 m and the distance from a harbor no more than five nautical miles. These were included along with maps of various environmental and socio-economic constraints in a Spatial Multi-Criteria Evaluation (SMCE). Touristic traffic and bottom trawling by fisherman were found to be the two other most restrictive variables. The GIS-based SMCE developed in this study showed that there is almost 400 km2 of highly- to very highly-suitable area within which to develop offshore aquaculture using simple, low-cost bottom-cage techniques, and can be used to assist the shellfish industry in the Marine Spatial Planning decision-making process, still in progress in this coastal area. However, the complexity of the administrative processes necessary to obtain an offshore license is perceived as a stronger barrier by farmers owning small companies than site selection, technical feasibility, and required investments, and will be crucial to address in order to realistically proceed to offshore cultivation. The process demonstrated here, and the results are relevant to other coastal and offshore locations throughout the world and can be adapted for other species.

Site Suitability for Finfish Marine Aquaculture in the Central Mediterranean Sea

Farm site selection plays a critical role in determining the productivity, environmental impact, and interactions of aquaculture activities with ecosystem services. Satellite Remote Sensing (SRS) provide spatially extensive datasets at high temporal and spatial resolution, which can be useful for aquaculture site selection. In this paper we mapped a finfish aquaculture suitability index applying the Spatial Multi-Criteria Evaluation (SMCE) methodology. The robustness of the outcome of the SMCE was investigated using Uncertainty Analysis (UA), and in parallel we evaluate a set of alternative scenarios, aimed at minimising the subjectivity associated with the decision process. The index is based on the outputs of eco-physiological models, which were forced using time series of sea surface temperature data, and on data concerning significant wave height, distance to harbour, current sea uses, and cumulative impacts. The methodology was applied to map the suitability for farming of European seabass (Dicentrarchus labrax) and gilthead seabream (Sparus aurata) within the Italian Economic Exclusive Zone (EEZ), under three scenarios: Blue Growth, Economic and Environment. Tyrrhenian and Ionian coastal areas were found to be more suitable, compared to the Northern Adriatic and southern Sicilian ones. In the latter, and in the western Sardinia, the index is also affected by higher uncertainty. The application presented suggests that SRS data could play a significant role in designing the Allocated Zones for Aquaculture, assisting policy makers and regulators in including aquaculture within marine spatial planning.

Modeling ENSO impact on rice production in the Mekong River Delta.

The Mekong River Delta is the rice production hub in South-east Asia and has a key role in determining rice prices in the world market. The increasing variability in the local climate due to global climate changes and the increasing severity of the ENSO phenomenon threatens rice production in the region, which has consequences for local and global food security. Though existing mapping efforts delineate the consequences of saline water intrusion during El Niño and flooding events during La Niña in the basin, research to predict future impacts in rice production is rather limited. The current work uses ORYZA, an ecophysiological model, combined with historical climate data, climate change scenarios RCP4.5 and 8.5 and climate-related risk maps to project the aggregate productivity and rice production impacts by the year 2050. Results show that in years of average salinity intrusion and flooding, the winter-spring rice crop in the MRD would experience an average annual decrease of 720,450 tons for 2020–2050 under the RCP4.5 scenario compared to the baseline of 2005–2016 average and another 1.17 million tons under the RCP8.5 scenario. The autumn-winter crop would decrease by 331,480 tons under RCP4.5 and 462,720 tons under RCP8.5. In years of severe salinity intrusion and flooding, the winter-spring rice crop would decrease by 2.13 million tons (10.29% lower than the projection for an average year) under RCP4.5 and 2.5 million tons (13.62%) under RCP8.5. Under severe conditions, the autumn-winter crop would have an average decrease of 1.3 million tons (7.36%) under RCP4.5 and 1.4 million tons (10.88%) for the RCP8.5 scenario. Given that most of the rice produced in this area is exported, a decline in rice supply at this scale would likely have implications on the global market price of rice affecting global food security. Such decline will also have implications for the rural economy and food security of Vietnam. Suggestions for corrective measures to reduce the impacts are briefly discussed.

Model-based QTL detection is sensitive to slight modifications in model formulation.

Classical crop models have been developed to predict crop yield and quality, and they are based on physiological and environmental inputs. After molecular discoveries, models should integrate genetic variation to allow predictions that are more genotype-dependent. An interesting approach, Quantitative Trait Locus (QTL)-based ecophysiological modeling, has shown promising results for the design of ideotypes that are adapted to biotic and abiotic stresses, but there are still limitations to attaining a fully integrated model. The aim of this case study is to clarify the impact of choosing different model equations (closely related and with different numbers of parameters) and optimization methods on the detection of QTLs controlling the parameters of crop growth. Different growth equations were parameterized based on a genetic population by following different approaches. The correlations between parameters were analyzed, and two different strategies were adopted to address the correlation issue. QTL analysis was performed on the optimized values of the parameters of the growth equations and on the observed dry mass (DM) data to validate the QTLs detected. Overall, models and strategies resulted in different QTLs being detected. Similar LOD profiles but with peaks of different heights were observed, some of which were significant, resulting in different numbers of QTLs. In some cases, peaks had slightly different positions or were absent. Even closely related growth models led to the detection of different QTLs. The goodness of fit and complexity of the growth models were found to be insufficient to select the best model. Calculating parameters independently of observed data may not be a good strategy, whereas setting parameters independent of the genotype is recommended. Given the large-scale global optimization problem and the strong correlations between parameters, the two algorithms tested showed poor performance. Currently, the lack of effective algorithms is the main obstacle to answering the question posed. The authors therefore suggest testing different model formulations and comparing the QTLs detected before choosing the best formulation to use in an ecophysiological modeling approach based on QTLs.

Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications

Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health.

Modification of a photosynthetic light-response (PLR) model for modeling the vertical gradient in the response of crown PLR curves

The photosynthetic light-response (PLR) curve is a mathematical description of a single biochemical process that has been widely applied in many ecophysiological models. For trees, the heterogeneity of PLR curves within the crown is significant but rarely modeled by mathematical techniques. This paper establishes a modified model for estimating crown PLR curves based on PLR functions by linking the parameters of the PLR functions to leaf nitrogen (N), specific leaf area (SLA), and relative depth into the crown (RDINC). The modified models were assessed by considering the goodness of fit (adjusted coefficient of determination, [Formula: see text]; root mean square error, RMSE; and Akaike information criterion, AIC) and model structure. Significant correlations were observed between the parameters of PLR functions and N, SLA, and RDINC. The optimal modified PLR model, by linking RDINC into a modified Mitscherlich function, fit well due to its simple and easily understood structure. Therefore, it is feasible to simultaneously estimate the multilayered and varied PLR curves of the tree crown.