Investigations into a plankton population model: Mortality and its importance in climate change scenarios

The potential for marine plankton ecosystems to influence climate by the production of dimethylsulphide (DMS) has been an important topic of recent research into climate change. Several General Circulation Models, used to predict climate change, have or are being modified to include interactions of ecosystems with climate. Climate change necessitates that parameters within ecosystem models must change during long-term simulations, especially mortality parameters that increase as organisms are pushed toward the boundaries of their thermal tolerance. Changing mortality parameters can have profound influences on ecosystem model dynamics. There is therefore a pressing need to understand the influence of varying mortality parameters on the long-term behaviour of ecosystem models. This work examines the sensitivity of a model of DMS production by marine ecosystems to variations in three linear mortality coefficients. Significant differences in behaviour are observed, and we note the importance of these results in formulating ecosystem models for application in simulations of climate change

Polarization modes for strong-field gravitational waves

Strong-field gravitational plane waves are often represented in either the Rosen or Brinkmann forms. These forms are related by a coordinate transformation, so they should describe essentially the same physics, but the two forms treat polarization states quite differently. Both deal well with linear polarizations, but there is a qualitative difference in the way they deal with circular, elliptic, and more general polarization states. In this article we will describe a general algorithm for constructing arbitrary polarization states in the Rosen form.Comment: 4 pages. Prepared for the proceedings of ERE2010 (Granada, Spain

Dimethylsulphide, clouds, and phytoplankton: Insights from a simple plankton ecosystem feedback model

The hypothesis that marine plankton ecosystems may effectively regulate climate by the production of dimethylsulphide (DMS) has attracted substantial research effort over recent years. This hypothesis suggests that DMS produced by marine ecosystems can affect cloud properties and hence the averaged irradiance experienced by the phytoplankton that produce DMS’s precursor dimethylsulphoniopropionate (DMSP). This paper describes the use of a simple model to examine the effects of such a biogenic feedback on the ecosystem that initiates it. We compare the responses to perturbation of a simple marine nitrogen-phytoplankton-zooplankton (NPZ) ecosystem model with and without biogenic feedback. Our analysis of this heuristic model reveals that the addition of the feedback can increase the model’s resilience to perturbation and hence stabilize the model ecosystem. This result suggests the hypothesis that DMS may play a role in stabilizing marine plankton ecosystem dynamics through its effect on the atmosphere

Modeling dimethylsulphide production in the upper ocean

Dimethylsulphide (DMS) is produced by upper ocean ecosystems and emitted to the atmosphere, where it may have an important role in climate regulation. Several attempts to quantify the role of DMS in climate change have been undertaken in modeling studies. We examine a model of biogenic DMS production and describe its endogenous dynamics and sensitivities. We extend the model to develop a one-dimensional version that more accurately resolves the important processes of the mixed layer in determining the ecosystem dynamics. Comparisons of the results of the one-dimensional model with an empirical relationship that describes the global distribution of DMS, and also with vertical profiles of DMS in the upper ocean measured at the Bermuda Atlantic Time Series, suggest that the model represents the interaction between the biological and physical processes well on local and global scales. Our analysis of the model confirms its veracity and provides insights into the important processes determining DMS concentration in the oceans

A novel unsupervised Levy flight particle swarm optimization (ULPSO) method for multispectral remote-sensing image classification

The rapid development of earth observation technology has produced large quantities of remote-sensing data. Unsupervised classification (i.e. clustering) of remote-sensing images, an important means to acquire land-use/cover information, has become increasingly in demand due to its simplicity and ease of application. Traditional methods, such as k-means, struggle to solve this NP-hard (Non-deterministic Polynomial hard) image classification problem. Particle swarm optimization (PSO), always achieving better result than k-means, has recently been applied to unsupervised image classification. However, PSO was also found to be easily trapped on local optima. This article proposes a novel unsupervised Levy flight particle swarm optimization (ULPSO) method for image classification with balanced exploitation and exploration capabilities. It benefits from a new searching strategy: the worst particle in the swarm is targeted and its position is updated with Levy flight at each iteration. The effectiveness of the proposed method was tested with three types of remote-sensing imagery (Landsat Thematic Mapper (TM), Flightline C1 (FLC), and QuickBird) that are distinct in terms of spatial and spectral resolution and landscape. Our results showed that ULPSO is able to achieve significantly better and more stable classification results than k-means and the other two intelligent methods based on genetic algorithm (GA) and particle swarm optimization (PSO) over all of the experiments. ULPSO is, therefore, recommended as an effective alternative for unsupervised remote-sensing image classification

A novel unsupervised bee colony optimization (UBCO) method for remote sensing image classification:a case study in a heterogeneous marsh area

Unsupervised image classification is an important means to obtain land use/cover information in the field of remote sensing, since it does not require initial knowledge (training samples) for classification. Traditional methods such as k-means and ISODATA have limitations in solving this NP-hard unsupervised classification problem, mainly due to their strict assumptions about the data distribution. The bee colony optimization (BCO) is a new type of swarm intelligence, based upon which a simple and novel unsupervised bee colony optimization (UBCO) method is proposed for remote sensing image classification. UBCO possesses powerful exploitation and exploration capacities that are carried out by employed bees, onlookers and scouts. This enables the promising regions to be globally searched quickly and thoroughly, without becoming trapped on local optima. In addition, it has no restrictions on data distribution, and thus is especially suitable for handling complex remote sensing data. We tested the method on the Zhalong National Nature Reserve (ZNNR)—a typical inland wetland ecosystem in China, whose landscape is heterogeneous. The preliminary results showed that UBCO (overall accuracy = 80.81%) achieved statistically significant better classification result (McNemar test) in comparison with traditional k-means (63.11%) and other intelligent clustering methods built on genetic algorithm (UGA, 71.49%), differential evolution (UDE, 77.57%) and particle swarm optimization (UPSO, 69.86%). The robustness and superiority of UBCO were also demonstrated from the two other study sites next to the ZNNR with distinct landscapes (urban and natural landscapes). Enabling to consistently find the optimal or nearly optimal global solution in image clustering, the UBCO is thus suggested as a robust method for unsupervised remote sensing image classification, especially in the case of heterogeneous areas

Parameterizing the impact of seawater temperature and irradiance on dimethylsulfide (DMS) in the Great Barrier Reef and the contribution of coral reefs to the global sulfur cycle

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Jackson, R. L., Gabric, A. J., Matrai, P. A., Woodhouse, M. T., Cropp, R., Jones, G. B., Deschaseaux, E. S. M., Omori, Y., McParland, E. L., Swan, H. B., & Tanimoto, H. Parameterizing the impact of seawater temperature and irradiance on dimethylsulfide (DMS) in the Great Barrier Reef and the contribution of coral reefs to the global sulfur cycle. Journal of Geophysical Research:Oceans, 126(3), (2021): e2020JC016783, https://doi.org/10.1029/2020JC016783.Biogenic emissions of dimethylsulfide (DMS) are an important source of sulfur to the atmosphere, with implications for aerosol formation and cloud albedo over the ocean. Natural aerosol sources constitute the largest uncertainty in estimates of aerosol radiative forcing and climate and thus, an improved understanding of DMS sources is needed. Coral reefs are strong point sources of DMS; however, this coral source of biogenic sulfur is not explicitly included in climatologies or in model simulations. Consequently, the role of coral reefs in local and regional climate remains uncertain. We aim to improve the representation of tropical coral reefs in DMS databases by calculating a climatology of seawater DMS concentration (DMSw) and sea-air flux in the Great Barrier Reef (GBR), Australia. DMSw is calculated from remotely sensed observations of sea surface temperature and photosynthetically active radiation using a multiple linear regression model derived from field observations of DMSw in the GBR. We estimate that coral reefs and lagoon waters in the GBR (∼347,000 km2) release 0.03–0.05 Tg yr−1 of DMS (0.02 Tg yr−1 of sulfur). Based on this estimate, global tropical coral reefs (∼600,000 km2) could emit 0.08 Tg yr−1 of DMS (0.04 Tg yr−1 of sulfur), with the potential to influence the local radiative balance.Australian Research Council. Grant Number: DP150101649 National Science Foundation (NSF). Grant Number: 1543450 Ministry of Education, Culture, Sports, Science and Technology Grants-in-Aid for Scientific Research. Grant Number: 23310016,16H02967,24241010,15H01732 Ministry of Education, Culture, Sports, Science and Technology Grant-in-Aid for Young Scientists. Grant Number: 17K1281

The emergence of new trophic levels in eco-evolutionary models with naturally-bounded traits

Ecosystems and food webs are structured into trophic levels of who eats whom. Species that occupy higher trophic levels have less available energy and higher energetic costs than species at lower trophic levels. So why do higher trophic levels exist? What processes generate new trophic levels? We consider a heuristic eco-evolutionary model based on simple Lotka-Volterra equations, where the evolution of traits is described by a generalisation of Lande's equation. The transition from competition to predation in this simplest of models is a successful, safe strategy for a population, and suggests a propensity to develop new trophic levels may be an inherent property of ecosystems. Numerical simulations with a more complex eco-evolutionary model of interacting plant and herbivore populations display the emergence of a new trophic level as an alternative to continued competition. These simulations reveal that new trophic levels may arise naturally from ecosystems because a robust strategy for a population in the presence of a strong competitor that could dominate or potentially extinguish them, is to predate upon the competitor. The same properties that make the competitor strong make it an ideal prey, suggesting the rubric that it is better to eat a strong competitor than to continue competing