Toxin-allelopathy among phytoplankton species prevents competitive exclusion

Toxic or allelopathic compounds liberated by toxin-producing phytoplankton (TPP) acts as a strong mediator in plankton dynamics. On an analysis of a set of phytoplankton biomass-data that have been collected by our group in the North-West part of the Bay of Bengal, and by analysis of a three-component mathematical model under a constant as well as a stochastic environment, we explore the role of toxin-allelopathy in determining the dynamic behaviour of the competing-phytoplankton species. The overall results, based on analytical and numerical wings, demonstrate that toxin-allelopathy due to the toxin-producing phytoplankton (TPP) promotes a stable coexistence of those competitive phytoplankton that would otherwise exhibit competitive exclusion of the weak species. Our study suggests that TPP might be a potential candidate for maintaining the coexistence and diversity of competing phytoplankton species.Comment: 29 pages, 6 figures, Journal Pape

Importance of allelopathy as peudo-mixotrophy for the dynamics and diversity of phytoplankton

Understanding the dynamics and diversity of marine phytoplankton is essential for predicting oceanic primary production, oxygen generation and carbon sequestration. Several top-down and bottom-up factors lead to complex phytoplankton dynamics. Complexities further arise from inter-species interactions within phytoplankton communities. Consequently, some of the basic questions on phytoplankton diversity, identified long ago, still puzzle the ecologists: for example, what regulates the diversity in simple systems where species compete for limiting resources? In this context, allelopathic interaction among phytoplankton species has been identified as a potential driver of their dynamics and regulator of their diversity. This chapter deals with the importance of allelopathy in regulating the outcome of nutrient competition among phytoplankton species, through analysis of a resource-competition model. It demonstrates that, through the mechanism of pseudo-mixotrophy - proposed earlier by the author - allelopathy provides essential growth advantage to weaker competitors, and stabilizes resource competition, which ensures the coexistence of two phytoplankton on a single nutrient. In simple nutrient-phytoplankton interactions where higher-trophic influences are negligible, this mechanism theoretically promotes phytoplankton diversity, and can potentially support high diversity in natural phytoplankton communities

Size-partitioned phytoplankton carbon and carbon-to-chlorophyll ratio from ocean colour by an absorption-based bio-optical algorithm

The standing stock of phytoplankton carbon is a fundamental property of oceanic ecosystems, and of critical importance to the development of Earth System models for assessing global carbon pools and cycles. Some methods to estimate phytoplankton carbon at large scales from ocean-colour data rely on the parameterization of carbon-to-chlorophyll ratio, which is known to depend on factors such as the phytoplankton community structure, whereas other methods are based on the estimation of total particulate organic carbon (POC), and rely on the assumption that a known fraction of POC is made up of phytoplankton carbon. The carbon-to-chlorophyll ratio is also used in marine ecosystem models to convert between carbon and chlorophyll, a common requirement. In this paper we present a novel bio-optical algorithm to estimate the carbon-to-chlorophyll ratio, and the standing stocks of phytoplankton carbon partitioned into various size classes, from ocean colour. The approach combines empirical allometric relationships of phytoplankton size structure with an absorption-based algorithm for estimating phytoplankton size spectra developed earlier. Applying the new algorithm to satellite ocean-colour data from September 1997 to December 2013, the spatio-temporal variations of carbon-to-chlorophyll ratio and phytoplankton carbon across various size classes are computed on a global scale. The average annual stock of phytoplankton carbon, integrated over the oceanic mixed-layer depth, is estimated to be ~0.26 gigatonnes, with the size-partitioned stocks of 0.14 gigatonnes for picoplankton, 0.08 gigatonnes for nanoplankton and 0.04 gigatonnes for microplankton. The root-mean-square error and the bias in the satellite-derived estimates of picoplankton carbon, when compared with corresponding in situ data, are found to be 36.23 mgC m-3 and -13.53 mgC m-3, respectively, on individual pixels. The regional uncertainties in the estimates of phytoplankton carbon are calculated to be less than the relative uncertainties in other satellite-derived products, for most parts of the global ocean, and can amplify only for certain oceanographic regions. Although the new estimates of phytoplankton are of the same order of magnitude as those based on existing models, our study suggests that a consensus is yet to be built on the accurate sizes of the phytoplankton carbon pools; improved satellite chlorophyll products, and better estimates of inherent optical properties would be essential pre-requisites to minimising the uncertainties

Computer code for 'Ecological determinants of Cope's rule and its inverse'

This dataset includes a computer code written in MATLAB required to run the eco-evolutionary model with two adaptive traits presented in Roy et al. (2023, Communications Biology). The code includes an executable programme, and four MATLAB subroutines that need to be called in within the programme to run the model. This code can be adapted to generate the evolutionary trajectories and outcome of the model under user-defined parameter combinations and scenarios described in details in Roy et al. (2023)

A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations

The dynamics of biogeochemical models are determined by the mathematical equations used to describe the main biological processes. Earlier studies have shown that small changes in the model formulation may lead to major changes in system dynamics, a property known as structural sensitivity. We assessed the impact of structural sensitivity in a biogeochemical model of intermediate complexity by modelling the chlorophyll and dissolved inorganic nitrogen (DIN) concentrations. The model is run at five different oceanographic stations spanning three different regimes: oligotrophic, coastal, and the abyssal plain, over a 10-year timescale to observe the effect in different regions. A 1-D MEDUSA ensemble was used with each ensemble member having a combination of tuned function parameterizations that describe some of the key biogeochemical processes, namely nutrient uptake, zooplankton grazing, and plankton mortalities. The impact is quantified using phytoplankton phenology (initiation, bloom time, peak height, duration, and termination of phytoplankton blooms) and statistical measures such as RMSE, mean, and range for chlorophyll and nutrients. The spread of the ensemble as a measure of uncertainty is assessed against observations using the Normalised RMSE Ratio (NRR).We found that even small perturbations in model structure can produce large ensemble spreads. The range of 10-year mean surface chlorophyll concentration in the ensemble is between 0.14-3.69 mg m-3 at coastal stations, 0.43-1.11 mg m-3 on the abyssal plain, and 0.004-0.16 mg m-3 at the oligotrophic stations. Changing both phytoplankton and zooplankton mortalities and the grazing functions have the largest impact on chlorophyll concentrations. The in situ measurements of bloom timings, duration, and terminations lie mostly within the ensemble range. The RMSEs between in situ observations and the ensemble mean and median are mostly reduced compared to the default model output. The NRRs for monthly variability suggest that the ensemble spread is generally narrow (NRR 1.21-1.39 for DIN and 1.19-1.39 for chlorophyll profiles, 1.07-1.40 for surface chlorophyll, and 1.01-1.40 for depth integrated chlorophyll). Among the five stations, the most reliable ensembles are obtained for the oligotrophic station ALOHA (for the surface and integrated chlorophyll and bloom peak height), for coastal station L4 (for inter-annual mean), and for the abyssal plain station PAP (for bloom peak height). Overall our study provides a novel way to generate a realistic ensemble of a biogeochemical model by perturbing the model equations/parameterizations, which will be helpful for the probabilistic predictions

A general approach to incorporating spatial and temporal variation in individual-based models of fish populations with application to Atlantic mackerel

Fish population dynamics are affected by multiple ecosystem drivers, such as food-web interactions, exploitation, density-dependence and the wider environment. While tactical management is still dominated by single-species models that do not explicitly account for these drivers, more holistic ecosystem models are used in strategic management. One way forward in this regard is with individual-based models (IBMs), which provide a single framework in which these drivers can be represented explicitly. We present a generic marine fish IBM that incorporates spatial and temporal variation in food availability, temperature and exploitation. Key features of the model are that it (1) includes realistic energy budgets; (2) includes the full life cycle of fish; (3) is spatially-explicit and (4) incorporates satellite remote-sensing data to represent the environmental drivers. The rates at which individuals acquire and use energy depend on local food availability and temperature. Their state variables, including life stage, size and energy reserves, are updated daily, from which population structure and dynamics emerge. To demonstrate the use of the model we calibrate it for mackerel (Scomber scombrus) in the North East Atlantic. Most parameters are taken from the literature, except the background early mortality rate and the strength predator density dependence, which were estimated by fitting the model to data using Approximate Bayesian Computation. The calibrated model successfully matches the available data on mackerel population dynamics and structure. We demonstrate the use of the model for management purposes by simulating the population effects of opening and closing a sector of the North Sea to mackerel fishing. Our model uses basic principles of behavioural and physiological ecology to establish how spatial and temporal variations in ecosystem drivers affect the individuals in the population. Population dynamics and structure are calculated from the collective effects on individuals. Application to a test case shows the method can fit available data well. Individual-based approaches such as this study have potential for use in strategic management because they can account for spatial structuring, food-web interactions, density dependence, and environmental drivers within a single framework

Perturbed biology and physics signatures in a 1-D ocean biogeochemical model ensemble

Sources of uncertainty in a marine biogeochemical model include input from physical processes and the choice of functional forms representing the strength and dependencies of biogeochemical processes. This study explores characteristic signatures from these uncertainties by generating ensembles from perturbing the biogeochemistry equations and perturbing physical input using a 1-D intermediately-complex model run at five oceanographic stations. Perturbed biogeochemistry ensemble (PBE) produces larger spreads than perturbed physics ensemble (PPE), and distinctly different ensemble variations. Fractions of nitrogen in phytoplankton pool from observations show a larger variability than in any single model-ensemble member, but the PBE spread generally captures this variability, whereas the PPE spread does not. The results show that the PBE method gives a more realistic representation of uncertainty than PPE in our 1D-model setup. Our method needs to be tested in more complex models in order to understand its significance on larger scales

Lake water acidification and temperature have a lagged effect on the population dynamics of Isoëtes echinospora via offspring recruitment

The aquatic quillwort, Isoëtes echinospora, survived the strong water acidification during 1960s–1990s in Plešné Lake (Bohemian Forest, Central Europe), but failed to reproduce. We studied the relationships between a recent population recovery and an improvement of lake water quality. We used correlation analysis to evaluate lagged seasonal effects of lake water quality on population dynamics during the past decade, and factor analysis to determine the independent factors responsible for population recovery. We also provided a water-quality-based reconstruction of population growth from the beginning of the lake recovery two decades ago, using a partial least squares regression (PLSR) model of population growth. We identified three independent controlling factors: nutrients (nitrate, phosphorus, calcium, potassium, magnesium), stressors (pH, ionic aluminium) and temperature. Of these, nutrient availability did not limit the quillwort growth, but annual mean pH and winter mean concentrations of toxic ionic aluminium influenced population growth through negative effects on sporeling establishment until the age of one year, while cumulative temperature in spring and summer controlled the later plant growth. Thus, water quality in the acidified Plešné Lake mainly controls recruitment success rather than adult survival of Isoëtes echinospora. This study provides the first in situ evidence that the recruitment success, namely the annual increment in the adult quillwort population, indicates the degree of recovery from acidification, however further extensive investigation is required to more accurately quantify, and therefore understand, the relationships between recruitment, water quality and other factors

Seasonal changes in water quality and Sargassum biomass in southwest Australia

Sargassum C. Agardh is one of the most diverse genera of marine macro-algae and commonly inhabits shallow tropical and sub-tropical waters. This study aimed to investigate the effect of seasonality and the associated water quality changes on the distribution, canopy cover, mean thallus length and the biomass of Sargassum beds around Point Peron, Shoalwater Islands Marine Park, Southwest Australia. Samples of Sargassum and seawater were collected every three months from summer 2012 to summer 2014 from four different reef zones. A combination of in situ observations and WorldView-2 satellite remote-sensing images were used to map the spatial distribution of Sargassum beds and other associated benthic habitats. The results demonstrated a strong seasonal variation in the environmental parameters, canopy cover, mean thallus length, and biomass of Sargassum, which were significantly (P < 0.05) influenced by the nutrient concentration (PO43-, NO3-, NH4+) and rainfall. However, no variation in any studied parameter was observed among the four reef zones. The highest Sargassum biomass peaks occurred between late spring and early summer (from September to January). The results provide essential information to guide effective conservation and management, as well as sustainable utilisation of this coastal marine renewable resource

Insect pollination as an agronomic input: strategies for oilseed rape production

1.Ecological intensification involves the incorporation of biodiversity based ecosystem service management into farming systems in order to make crop production more sustainable and reduce reliance on anthropogenic inputs, including fertiliser and insecticides. 2.The benefits of effectively managing ecosystem services such as pollination and pest regulation for improved yields have been demonstrated in a number of studies, however recent evidence indicates that these benefits interact with conventional agronomic inputs such as fertiliser and irrigation. Despite the important contribution of biodiversity‐based ecosystem services to crop production their management is rarely considered in combination with more conventional agronomic inputs. 3.This study combines a number of complementary approaches to evaluate the impact of insect pollination on yield parameters of Brassica napus and how this interacts with a key agronomic input, fertiliser. We incorporate data from a flight cage trial and multiple field studies to quantify the relationships between yield parameters to determine whether insufficient insect pollination may limit crop yield. 4.We demonstrate that, by producing larger seeds and more pods, B. napus has the capacity to modulate investment across yield parameters and buffer sub‐optimal inputs of fertiliser or pollination. However, only when fertiliser is not limiting can the crop benefit from insect pollination, with yield increases due to insect pollination only seen under high fertiliser application. 5.A non‐linear relationship between seed set per pod and yield per plant was found, with increases in seed set between 15 and 25 seeds per pod resulting in a consistent increase in crop yield. The capacity for the crop to compensate for lower seed set due to sub‐optimal pollination is therefore limited. 6.Synthesis and applications. Oilseed rape has the capacity to compensate for sub‐optimal agronomic or ecosystem service inputs although this has limitations. Insect pollination can increase seed set and so there are production benefits to be gained through effective management of wild pollinators or by utilising managed species. Our study demonstrates however that increased insect pollination cannot simply replace other inputs, and if resources such as fertiliser are limiting, then yield potential cannot be reached. We highlight the need to consider insect pollination as an agronomic input to be effectively managed in agricultural systems