Cost-benefit analysis of ecosystem modeling to support fisheries management.

Mathematical and statistical models underlie many of the world's most important fisheries management decisions. Since the 19th century, difficulty calibrating and fitting such models has been used to justify the selection of simple, stationary, single-species models to aid tactical fisheries management decisions. Whereas these justifications are reasonable, it is imperative that we quantify the value of different levels of model complexity for supporting fisheries management, especially given a changing climate, where old methodologies may no longer perform as well as in the past. Here we argue that cost-benefit analysis is an ideal lens to assess the value of model complexity in fisheries management. While some studies have reported the benefits of model complexity in fisheries, modeling costs are rarely considered. In the absence of cost data in the literature, we report, as a starting point, relative costs of single-species stock assessment and marine ecosystem models from two Australian organizations. We found that costs varied by two orders of magnitude, and that ecosystem model costs increased with model complexity. Using these costs, we walk through a hypothetical example of cost-benefit analysis. The demonstration is intended to catalyze the reporting of modeling costs and benefits.

Unlocking ensemble ecosystem modelling for large and complex networks.

The potential effects of conservation actions on threatened species can be predicted using ensemble ecosystem models by forecasting populations with and without intervention. These model ensembles commonly assume stable coexistence of species in the absence of available data. However, existing ensemble-generation methods become computationally inefficient as the size of the ecosystem network increases, preventing larger networks from being studied. We present a novel sequential Monte Carlo sampling approach for ensemble generation that is orders of magnitude faster than existing approaches. We demonstrate that the methods produce equivalent parameter inferences, model predictions, and tightly constrained parameter combinations using a novel sensitivity analysis method. For one case study, we demonstrate a speed-up from 108 days to 6 hours, while maintaining equivalent ensembles. Additionally, we demonstrate how to identify the parameter combinations that strongly drive feasibility and stability, drawing ecological insight from the ensembles. Now, for the first time, larger and more realistic networks can be practically simulated and analysed.

Quantifying the role of photoacclimation and self-facilitation for seagrass resilience to light deprivation.

Introduction: Light gradients are ubiquitous in marine systems as light reduces exponentially with depth. Seagrasses have a set of mechanisms that help them to cope with light stress gradients. Physiological photoacclimation and clonal integration help to maximize light capture and minimize carbon losses. These mechanisms can shape plants minimum light requirements (MLR), which establish critical thresholds for seagrass survival and help us predict ecosystem responses to the alarming reduction in light availability. Methods: Using the seagrass Cymodocea nodosa as a case study, we compare the MLR under different carbon model scenarios, which include photoacclimation and/or self-facilitation (based on clonal integration) and that where parameterized with values from field experiments. Results: Physiological photoacclimation conferred plants with increased tolerance to reducing light, approximately halving their MLR from 5-6% surface irradiance (SI) to ≈ 3% SI. In oligotrophic waters, this change in MLR could translate to an increase of several meters in their depth colonization limit. In addition, we show that reduced mortality rates derived from self-facilitation mechanisms (promoted by high biomass) induce bistability of seagrass meadows along the light stress gradient, leading to abrupt shifts and hysteretic behaviors at their deep limit. Discussion: The results from our models point to (i) the critical role of physiological photoacclimation in conferring greater resistance and ability to recover (i.e., resilience), to seagrasses facing light deprivation and (ii) the importance of self-facilitating reinforcing mechanisms in driving the resilience and recovery of seagrass systems exposed to severe light reduction events.

Leaching composition and associated microbial community of recycled concrete aggregate (RCA).

Recycled concrete aggregate (RCA) has been used as an alternative sustainable material in the construction industry, but RCA long-term environmental impacts are unknown. In this study, the bacterial enrichment potential to reduce the alkalinity of two different types of RCA was examined, from laboratory-produced concrete and from a stockpile of demolished concrete that had been in service in transportation applications. Washed and un-washed lab and field RCA were biostimulated by being exposed to ATCC® Medium 661 in batch experiments. pH, metal composition and microbial community changes in the leachates were monitored over time. Results show that initial pH of field RCA leachate could be decreased to less concerning values, as low as 8, but concentrations of some metals in the leachate exceeded groundwater quality standards. However, the biostimulated RCA released lower metal concentration and was more resistant to pH increases than non-biostimulated RCA during a long-term leaching experiment with DI water. The microbial community was enriched on anaerobic, halotolerant and alkaliphile microorganisms, resistant to extreme environmental conditions. The outcome of this research suggests a baseline for field RCA pretreatment before field application, using a biostimulation method that would generate a less environmentally detrimental runoff.

Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data.

This work introduces a comprehensive approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the combination of prior beliefs and data. This approach identifies stiff parameter combinations strongly affecting the quality of the model-data fit while simultaneously revealing which of these key parameter combinations are informed primarily by the data or are also substantively influenced by the priors. We focus on the very common context in complex systems where the amount and quality of data are low compared to the number of model parameters to be collectively estimated, and showcase the benefits of this technique for applications in biochemistry, ecology, and cardiac electrophysiology. We also show how stiff parameter combinations, once identified, uncover controlling mechanisms underlying the system being modeled and inform which of the model parameters need to be prioritized in future experiments for improved parameter inference from collective model-data fitting.

Integral, mean and covariance of the simplex-truncated multivariate normal distribution.

Compositional data, which is data consisting of fractions or probabilities, is common in many fields including ecology, economics, physical science and political science. If these data would otherwise be normally distributed, their spread can be conveniently represented by a multivariate normal distribution truncated to the non-negative space under a unit simplex. Here this distribution is called the simplex-truncated multivariate normal distribution. For calculations on truncated distributions, it is often useful to obtain rapid estimates of their integral, mean and covariance; these quantities characterising the truncated distribution will generally possess different values to the corresponding non-truncated distribution. In this paper, three different approaches that can estimate the integral, mean and covariance of any simplex-truncated multivariate normal distribution are described and compared. These three approaches are (1) naive rejection sampling, (2) a method described by Gessner et al. that unifies subset simulation and the Holmes-Diaconis-Ross algorithm with an analytical version of elliptical slice sampling, and (3) a semi-analytical method that expresses the integral, mean and covariance in terms of integrals of hyperrectangularly-truncated multivariate normal distributions, the latter of which are readily computed in modern mathematical and statistical packages. Strong agreement is demonstrated between all three approaches, but the most computationally efficient approach depends strongly both on implementation details and the dimension of the simplex-truncated multivariate normal distribution. For computations in low-dimensional distributions, the semi-analytical method is fast and thus should be considered. As the dimension increases, the Gessner et al. method becomes the only practically efficient approach of the methods tested here.

Connecting targets for catchment sediment loads to ecological outcomes for seagrass using multiple lines of evidence.

Catchment impacts on downstream ecosystems are difficult to quantify, but important for setting management targets. Here we compared 12 years of monitoring data of seagrass area and biomass in Cleveland Bay, northeast Australia, with discharge and associated sediment loads from nearby rivers. Seagrass biomass and area exhibited different trajectories in response to river inputs. River discharge was a slightly better predictor of seagrass indicators than total suspended solid (TSS) loads, indicating that catchment effects on seagrass are not restricted to sediment. Linear relationships between Burdekin River TSS loads delivered over 1-4 years and seagrass condition in Cleveland Bay generated Ecologically Relevant Targets (ERT) for catchment sediment inputs. Our predicted ERTs were comparable to those previously estimated using mechanistic models. This study highlights the challenges of linking catchment inputs to condition of downstream ecosystems, and the importance of integrating a variety of metrics and approaches to increase confidence in ERTs.

Improved motor outcome prediction in Parkinson's disease applying deep learning to DaTscan SPECT images.

PURPOSE: Dopamine transporter (DAT) SPECT imaging is routinely used in the diagnosis of Parkinson's disease (PD). Our previous efforts demonstrated the use of DAT SPECT images in a data-driven manner by improving prediction of PD clinical assessment outcome using radiomic features. In this work, we develop a convolutional neural network (CNN) based technique to predict clinical motor function evaluation scores directly from longitudinal DAT SPECT images and non-imaging clinical measures. PROCEDURES: Data of 252 subjects from the Parkinson's Progression Markers Initiative (PPMI) database were used in this work. The motor part (III) score of the unified Parkinson's disease rating scale (UPDRS) at year 4 was selected as outcome, and the DAT SPECT images and UPDRS_III scores acquired at year 0 and year 1 were used as input data. The specified inputs and outputs were used to develop a CNN based regression method for prediction. Ten-fold cross-validation was used to test the trained network and the absolute difference between predicted and actual scores was used as the performance metric. Prediction using inputs with and without DAT images was evaluated. RESULTS: Using only UPDRS_III scores at year 0 and year 1, the prediction yielded an average difference of 7.6 ± 6.1 between the predicted and actual year 4 motor scores (range [5, 77]). The average difference was reduced to 6.0 ± 4.8 when longitudinal DAT SPECT images were included, which was determined to be statistically significant via a two-sample t-test, and demonstrates the benefit of including images. CONCLUSIONS: This study shows that adding DAT SPECT images to UPDRS_III scores as inputs to deep-learning based prediction significantly improves the outcome. Without requiring segmentation and feature extraction, the CNN based prediction method allows easier and more universial application.

Plant morphology and seed germination responses of seagrass (Zostera japonica) to water depth and light availability in Ailian Bay, northern China.

Poor water quality and light reduction owing to anthropogenic impacts are the most widespread causes behind marine submerged angiosperm (seagrass) declines, worldwide. Seagrasses could respond to sustained environmental stresses, such as increasing water depth and light reduction, through morphological changes, particularly shoot density and/or biomass reductions. The seagrass Zostera japonica Asch. and Graebn. has been introduced to the Pacific Coast of North America, but it is widely threatened in its native northwestern Pacific Coast range alongside the east coast of China. The main aims of this study were to determine: 1) the depth limit of Z. japonica growth in its native range, and 2) how light availability affects the growth and recruitment of Z. japonica. To achieve these aims, we investigated the temporal responses of Z. japonica shoots and seeds from an intertidal donor site, Swan Lake, to light availability at water depths ranging from 1 to 6 m using in situ suspended cultures deployed in the experimental site, Ailian Bay, off the coast of Weihai City, China. The results showed that the transplanted Z. japonica shoots and seeds could survive for the duration of their annual growth cycle, permanently underwater, at a depth ≤2 m. There was a significant inverse relationship between water depth and time to complete shoot loss, despite temporally varying water clarity levels. Due to the local turbidity of the waters in Ailian Bay, a depth of 2 m yielded sufficient light deprivation (5%-37% surface irradiance) to negatively affect the seagrass shoot density. Our results suggest that this intertidal species can potentially persist in shallow subtidal areas following transplantation with shoots and seeds. The findings may also serve as useful information for local seagrass distribution limits, and will facilitate their habitat establishment and restoration efforts.

Are laboratory growth rate experiments relevant to explaining bloom-forming cyanobacteria distributions at global scale?

Predicting algal population dynamics using models informed by experimental data has been used as a strategy to inform the management and control of harmful cyanobacterial blooms. We selected toxic bloom-forming species Microcystis spp. and Raphidiopsis raciborskii (basionym Cylindrospermopsis raciborskii) for further examination as they dominate in 78 % and 17 %, respectively, of freshwater cyanobacterial blooms (cyanoHABs) reported globally over the past 30 years. Field measurements of cyanoHABs are typically based on biomass accumulation, but laboratory experiments typically measure growth rates, which are an important variable in cyanoHAB models. Our objective was to determine the usefulness of laboratory studies of these cyanoHAB growth rates for simulating the species dominance at a global scale. We synthesized growth responses of M. aeruginosa and R. raciborskii from 20 and 16 culture studies, respectively, to predict growth rates as a function of two environmental variables, light and temperature. Predicted growth rates of R. raciborskii exceeded those of M. aeruginosa at temperatures ≳ 25 °C and light intensities ≳ 150 µmol photons m-2 s-1. Field observations of biomass accumulation, however, show that M. aeruginosa dominates over R. raciborskii, irrespective of climatic zones. The mismatch between biomass accumulation measured in the field, and what is predicted from growth rate measured in the laboratory, hinders effective use of culture studies to predict formation of cyanoHABs in the natural environment. The usefulness of growth rates measured may therefore be limited, and field experiments should instead be designed to examine key physiological attributes such as colony formation, buoyancy regulation and photoadaptation. Improving prediction of cyanoHABs in a changing climate requires a more effective integration of field and laboratory approaches, and an explicit consideration of strain-level variability.

Informing management decisions for ecological networks, using dynamic models calibrated to noisy time-series data.

Well-intentioned environmental management can backfire, causing unforeseen damage. To avoid this, managers and ecologists seek accurate predictions of the ecosystem-wide impacts of interventions, given small and imprecise datasets, which is an incredibly difficult task. We generated and analysed thousands of ecosystem population time series to investigate whether fitted models can aid decision-makers to select interventions. Using these time-series data (sparse and noisy datasets drawn from deterministic Lotka-Volterra systems with two to nine species, of known network structure), dynamic model forecasts of whether a species' future population will be positively or negatively affected by rapid eradication of another species were correct > 70% of the time. Although 70% correct classifications is only slightly better than an uninformative prediction (50%), this classification accuracy can be feasibly improved by increasing monitoring accuracy and frequency. Our findings suggest that models may not need to produce well-constrained predictions before they can inform decisions that improve environmental outcomes.

Night and day: Shrinking and swelling of stems of diverse mangrove species growing along environmental gradients.

Tree stems swell and shrink daily, which is thought to reflect changes in the volume of water within stem tissues. We observed these daily patterns using automatic dendrometer bands in a diverse group of mangrove species over five mangrove forests across Australia and New Caledonia. We found that mangrove stems swelled during the day and shrank at night. Maximum swelling was highly correlated with daily maxima in air temperature. Variation in soil salinity and levels of tidal inundation did not influence the timing of stem swelling over all species. Medium-term increases in stem circumference were highly sensitive to rainfall. We defoliated trees to assess the role of foliar transpiration in stem swelling and shrinking. Defoliated trees showed maintenance of the pattern of daytime swelling, indicating that processes other than canopy transpiration influence the temporary stem diameter increments, which could include thermal swelling of stems. More research is required to understand the processes contributing to stem shrinking and swelling. Automatic Dendrometer Bands could provide a useful tool for monitoring the response of mangroves to extreme climatic events as they provide high-frequency, long-term, and large-scale information on tree water status.

Artificial Neural Network-Based Prediction of Outcome in Parkinson's Disease Patients Using DaTscan SPECT Imaging Features.

PURPOSE: Quantitative analysis of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) images can enhance diagnostic confidence and improve their potential as a biomarker to monitor the progression of Parkinson's disease (PD). In the present work, we aim to predict motor outcome from baseline DAT SPECT imaging radiomic features and clinical measures using machine learning techniques. PROCEDURES: We designed and trained artificial neural networks (ANNs) to analyze the data from 69 patients within the Parkinson's Progressive Marker Initiative (PPMI) database. The task was to predict the unified PD rating scale (UPDRS) part III motor score in year 4 from 92 imaging features extracted on 12 different regions as well as 6 non-imaging measures at baseline (year 0). We first performed univariate screening (including the adjustment for false discovery) to select 4 regions each having 10 features with significant performance in classifying year 4 motor outcome into two classes of patients (divided by the UPDRS III threshold of 30). The leave-one-out strategy was then applied to train and test the ANNs for individual and combinations of features. The prediction statistics were calculated from 100 rounds of experiments, and the accuracy in appropriate prediction (classification of year 4 outcome) was quantified. RESULTS: Out of the baseline non-imaging features, only the UPDRS III (at year 0) was predictive, while multiple imaging features depicted significance. The different selected features reached a predictive accuracy of 70 % if used individually. Combining the top imaging features from the selected regions significantly improved the prediction accuracy to 75 % (p < 0.01). The combination of imaging features with the year 0 UPDRS III score also improved the prediction accuracy to 75 %. CONCLUSION: This study demonstrated the added predictive value of radiomic features extracted from DAT SPECT images in serving as a biomarker for PD progression tracking.

Improved myocardial perfusion PET imaging with MRI assisted reconstruction incorporating multi-resolution joint entropy.

Myocardial perfusion (MP) PET imaging plays an important role in risk assessment and stratification of patients with coronary artery disease. In this work, we developed an anatomy-assisted maximum a posteriori (MAP) reconstruction method incorporating a wavelet-based joint entropy (WJE) prior for MP PET imaging. Using the XCAT phantom, we first simulated three MP PET datasets, one with normal perfusion and the other two with non-transmural and transmural regionally reduced perfusion of the left ventricular myocardium. We then simulated MP PET datasets of the three cases with respiratory and cardiac (RC) motion to represent realistic clinical situations. Moreover, two MR image datasets of the same subjects without and with RC motion were simulated without the perfusion defect correspondence. Using the simulated data, the proposed method was evaluated quantitatively in terms of noise-contrast tradeoff, and compared with the post-smoothed maximum-likelihood and the conventional MAP methods. The detectability of perfusion defects with various myocardial coverage was also evaluated through receiver operating characteristic analysis using the channelized Hotelling observer. The results demonstrated that the WJE-MAP method improved the noise-contrast tradeoff, leading to significantly enhanced defect detectability over the other two methods in the non-transmural defects, while maintaining comparable performance in the transmural defect. In addition to the simulation study, the proposed method was further evaluated on the acquired PET/MRI data of a Jaszczak phantom with cold rods. Compared with the other two methods, the WJE-MAP method improved the tradeoff between noise and contrast in the smaller rods, thereby indicating its clinical potential for improving defect detectability in MP PET/MR imaging.

Losing a winner: thermal stress and local pressures outweigh the positive effects of ocean acidification for tropical seagrasses.

Seagrasses are globally important coastal habitat-forming species, yet it is unknown how seagrasses respond to the combined pressures of ocean acidification and warming of sea surface temperature. We exposed three tropical species of seagrass (Cymodocea serrulata, Halodule uninervis, and Zostera muelleri) to increasing temperature (21, 25, 30, and 35°C) and pCO2 (401, 1014, and 1949 µatm) for 7 wk in mesocosms using a controlled factorial design. Shoot density and leaf extension rates were recorded, and plant productivity and respiration were measured at increasing light levels (photosynthesis-irradiance curves) using oxygen optodes. Shoot density, growth, photosynthetic rates, and plant-scale net productivity occurred at 25°C or 30°C under saturating light levels. High pCO2 enhanced maximum net productivity for Z. muelleri, but not in other species. Z. muelleri was the most thermally tolerant as it maintained positive net production to 35°C, yet for the other species there was a sharp decline in productivity, growth, and shoot density at 35°C, which was exacerbated by pCO2 . These results suggest that thermal stress will not be offset by ocean acidification during future extreme heat events and challenges the current hypothesis that tropical seagrass will be a 'winner' under future climate change conditions.

Seasonal dynamics of trace elements in sediment and seagrass tissues in the largest Zostera japonica habitat, the Yellow River Estuary, northern China.

Trace element accumulation is an anthropogenic threat to seagrass ecosystems, which in turn may affect the health of humans who depend on these ecosystems. Trace element accumulation in seagrass meadows may vary temporally due to, e.g., seasonal patterns in sediment discharge from upstream areas. In addition, when several trace elements are present in sufficiently high concentrations, the risk of seagrass loss due to the cumulative impact of these trace elements is increased. To assess the seasonal variation and cumulative risk of trace element contamination to seagrass meadows, trace element (As, Cd, Cr, Cu, Pb, Hg, Mn and Zn) levels in surface sediment and seagrass tissues were measured in the largest Chinese Zostera japonica habitat, located in the Yellow River Estuary, at three sites and three seasons (fall, spring and summer) in 2014-2015. In all three seasons, trace element accumulation in the sediment exceeded background levels for Cd and Hg. Cumulative risk to Z. japonica habitat in the Yellow River Estuary, from all trace elements together, was assessed as "moderate" in all three seasons examined. Bioaccumulation of trace elements by seagrass tissues was highly variable between seasons and between above-ground and below-ground biomass. The variation in trace element concentration of seagrass tissues was much higher than the variation in trace element concentration of the sediment. In addition, for trace elements which tended to accumulate more in above-ground biomass than below-ground biomass (Cd and Mn), the ratio of above-ground to below-ground trace element concentration peaked at times corresponding to high water discharge and high sediment loads in the Yellow River Estuary. Overall, our results suggest that trace element accumulation in the sediment may not vary between seasons, but bioaccumulation in seagrass tissues is highly variable and may respond directly to trace elements in the water column.

Seagrass ecosystem trajectory depends on the relative timescales of resistance, recovery and disturbance.

Seagrass ecosystems are inherently dynamic, responding to environmental change across a range of scales. Habitat requirements of seagrass are well defined, but less is known about their ability to resist disturbance. Specific means of recovery after loss are particularly difficult to quantify. Here we assess the resistance and recovery capacity of 12 seagrass genera. We document four classic trajectories of degradation and recovery for seagrass ecosystems, illustrated with examples from around the world. Recovery can be rapid once conditions improve, but seagrass absence at landscape scales may persist for many decades, perpetuated by feedbacks and/or lack of seed or plant propagules to initiate recovery. It can be difficult to distinguish between slow recovery, recalcitrant degradation, and the need for a window of opportunity to trigger recovery. We propose a framework synthesizing how the spatial and temporal scales of both disturbance and seagrass response affect ecosystem trajectory and hence resilience.

Invasive Macrophytes Control the Spatial and Temporal Patterns of Temperature and Dissolved Oxygen in a Shallow Lake: A Proposed Feedback Mechanism of Macrophyte Loss.

Submerged macrophytes can have a profound effect on shallow lake ecosystems through their ability to modify the thermal structure and dissolved oxygen levels within the lake. Invasive macrophytes, in particular, can grow rapidly and induce thermal gradients in lakes that may substantially change the ecosystem structure and challenge the survival of aquatic organisms. We performed fine-scale measurements and 3D numerical modeling at high spatiotemporal resolution to assess the effect of the seasonal growth of Potamogeton crispus L. on the spatial and temporal dynamics of temperature and dissolved oxygen in a shallow urban lake (Lake Monger, Perth, WA, Australia). Daytime stratification developed during the growing season and was clearly observed throughout the macrophyte bed. At all times measured, stratification was stronger at the center of the macrophyte bed compared to the bed edges. By fitting a logistic growth curve to changes in plant height over time (r2 = 0.98), and comparing this curve to temperature data at the center of the macrophyte bed, we found that stratification began once the macrophytes occupied at least 50% of the water depth. This conclusion was strongly supported by a 3D hydrodynamic model fitted to weekly temperature profiles measured at four time periods throughout the growing season (r2 > 0.78 at all times). As the macrophyte height increased and stratification developed, dissolved oxygen concentration profiles changed from vertically homogeneous oxic conditions during both the day and night to expression of night-time anoxic conditions close to the sediments. Spatially interpolated maps of dissolved oxygen and 3D numerical modeling results indicated that the plants also reduced horizontal exchange with surrounding unvegetated areas, preventing flushing of low dissolved oxygen water out of the center of the bed. Simultaneously, aerial imagery showed central dieback occurring toward the end of the growing season. Thus, we hypothesized that stratification-induced anoxia can lead to accelerated P. crispus dieback in this region, causing formation of a ring-shaped pattern in spatial macrophyte distribution. Overall, our study demonstrates that submerged macrophytes can alter the thermal characteristics and oxygen levels within shallow lakes and thus create challenging conditions for maximizing their spatial coverage.

Variation within and between cyanobacterial species and strains affects competition: Implications for phytoplankton modelling.

Cyanobacteria Microcystis aeruginosa and Cylindrospermopsis raciborskii are two harmful species which co-occur and successively dominate in freshwaters globally. Within-species strain variability affects cyanobacterial population responses to environmental conditions, and it is unclear which species/strain would dominate under different environmental conditions. This study applied a Monte Carlo approach to a phytoplankton dynamic growth model to identify how growth variability of multiple strains of these two species affects their competition. Pairwise competition between four M. aeruginosa and eight C. raciborskii strains was simulated using a deterministic model, parameterized with laboratory measurements of growth and light attenuation for all strains, and run at two temperatures and light intensities. 17 000 runs were simulated for each pair using a statistical distribution with Monte Carlo approach. The model results showed that cyanobacterial competition was highly variable, depending on strains present, light and temperature conditions. There was no absolute 'winner' under all conditions as there were always strains predicted to coexist with the dominant strains, which were M. aeruginosa strains at 20°C and C. raciborskii strains at 28°C. The uncertainty in prediction of species competition outcomes was due to the substantial variability of growth responses within and between strains. Overall, this study demonstrates that within-species strain variability has a potentially large effect on cyanobacterial population dynamics, and therefore this variability may substantially reduce confidence in predicting outcomes of phytoplankton competition in deterministic models, that are based on only one set of parameters for each species or strain.

Optimum Temperatures for Net Primary Productivity of Three Tropical Seagrass Species.

Rising sea water temperature will play a significant role in responses of the world's seagrass meadows to climate change. In this study, we investigated seasonal and latitudinal variation (spanning more than 1,500 km) in seagrass productivity, and the optimum temperatures at which maximum photosynthesis and net productivity (for the leaf and the whole plant) occurs, for three seagrass species (Cymodocea serrulata, Halodule uninervis, and Zostera muelleri). To obtain whole plant net production, photosynthesis, and respiration rates of leaves and the root/rhizome complex were measured using oxygen-sensitive optodes in closed incubation chambers at temperatures ranging from 15 to 43°C. The temperature-dependence of photosynthesis and respiration was fitted to empirical models to obtain maximum metabolic rates and thermal optima. The thermal optimum (Topt) for gross photosynthesis of Z. muelleri, which is more commonly distributed in sub-tropical to temperate regions, was 31°C. The Topt for photosynthesis of the tropical species, H. uninervis and C. serrulata, was considerably higher (35°C on average). This suggests that seagrass species are adapted to water temperature within their distributional range; however, when comparing among latitudes and seasons, thermal optima within a species showed limited acclimation to ambient water temperature (Topt varied by 1°C in C. serrulata and 2°C in H. uninervis, and the variation did not follow changes in ambient water temperature). The Topt for gross photosynthesis were higher than Topt calculated from plant net productivity, which includes above- and below-ground respiration for Z. muelleri (24°C) and H. uninervis (33°C), but remained unchanged at 35°C in C. serrulata. Both estimated plant net productivity and Topt are sensitive to the proportion of below-ground biomass, highlighting the need for consideration of below- to above-ground biomass ratios when applying thermal optima to other meadows. The thermal optimum for plant net productivity was lower than ambient summer water temperature in Z. muelleri, indicating likely contemporary heat stress. In contrast, thermal optima of H. uninervis and C. serrulata exceeded ambient water temperature. This study found limited capacity to acclimate: thus the thermal optima can forewarn of both the present and future vulnerability to ocean warming during periods of elevated water temperature.