Massive Blooms of Chattonella subsalsa Biecheler (Raphidophyceae) in a Hypereutrophic, Tropical Estuary—Guanabara Bay, Brazil

Cell concentrations of the potentially harmful raphidophyte Chattonella subsalsa Biecheler were quantified in surface waters of Guanabara Bay, a heavily eutrophicated estuarine system in tropical Brazil, from February 2014 to January 2018. Cells were imaged and quantified in live samples by means of an automated imaging system (FlowCam®). Bloom episodes (>0.1 × 106 cells L−1) were observed in 37 samples, mostly in a shallow (<10 m) area with extremely high nutrient and organic matter loads (average total P = 19 μM and total N = 344 μM), intermediate salinity (average 24.5), and low water transparency (average Secchi depth = 0.54 m) due to continental runoff. Blooms in this area reached up to 13.3 × 106 cells L−1. C. subsalsa cell concentration was correlated with parameters linked to eutrophication of the bay. On a monthly basis, C. subsalsa abundance was correlated with a period of positive Multivariated El Niño/Southern Oscilation Index (MEI) that lasted from the beginning of 2015 to mid-2016 (known as Godzilla El Niño), indicating a potential influence of regional climate on the occurrence of C. subsalsa. Notably, at least six fish kill episodes were reported in the Bay during this period which, added to the toxicity of C. subsalsa strains isolated from the bay to Artemia nauplia (48h-LC50 = 7.3 × 106 cells L−1), highlights the threat that this HAB species poses to the environment. This is the first report of recurrent, massive C. subsalsa blooms in Guanabara Bay. Regardless of the influence of climatic forcing in favoring C. subsalsa development, reducing nutrient loads would be the best strategy to mitigate blooms of this and other potentially harmful algae in Guanabara Bay

Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests

The seasonal climate drivers of the carbon cycle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combination of seasonal pan-tropical data sets from 89 experimental sites (68 include aboveground wood productivity measurements and 35 litter productivity measurements), their associated canopy photosynthetic capacity (enhanced vegetation index, EVI) and climate, we ask how carbon assimilation and aboveground allocation are related to climate seasonality in tropical forests and how they interact in the seasonal carbon cycle. We found that canopy photosynthetic capacity seasonality responds positively to precipitation when rainfall is  < 2000 mm yr⁻¹ (water-limited forests) and to radiation otherwise (light-limited forests). On the other hand, independent of climate limitations, wood productivity and litterfall are driven by seasonal variation in precipitation and evapotranspiration, respectively. Consequently, light-limited forests present an asynchronism between canopy photosynthetic capacity and wood productivity. First-order control by precipitation likely indicates a decrease in tropical forest productivity in a drier climate in water-limited forest, and in current light-limited forest with future rainfall  < 2000 mm yr⁻¹

Optimal approximation schedules for iterative algorithms with application to dynamic programming

Many iterative algorithms rely on operators which may be difficult or impossible to evaluate exactly, but for which approximations are available. Furthermore, a graduated range of approximations may be available, inducing a functional relationship between computational complexity and approximation tolerance. In such a case, a reasonable strategy would be to vary tolerance over iterations, starting with a cruder approximation, then gradually decreasing tolerance as the solution is approached. In this article, it is shown that under general conditions, for linearly convergent algorithms the optimal choice of approximation tolerance convergence rate is the same linear convergence rate as the exact algorithm itself, regardless of the tolerance/complexity relationship. We illustrate this result by presenting a partial information value iteration (PIVI) algorithm for discrete time dynamic programming problems. The proposed algorithm makes use of increasingly accurate partial model information in order to decrease the computational burden of the standard value iteration algorithm. The algorithm is applied to a stochastic network example and compared to value iteration for the purpose of benchmarking.</p

Evaluating Brazilian Bid Rounds: The impact of a plan to grant licences to optimize demand in the upstream sector

The Brazilian oil and gas (O&amp;G) sector has huge potential to grow in the next few decades. Changes made by the Petroleum Law established the Brazilian Bid Round system to offer exploratory areas and to grant exploration and production (E&amp;P) licences, resulting in the expansion of investment in E&amp;P. Due to Petrobras' monopoly in the E&amp;P sector until 1998, only a small portion of Brazil's sedimentary basins (SBs) have been explored, leaving a large amount of exploratory data to be acquired in poorly explored areas, where little knowledge about hydrocarbon potential is available. This article proposes a model to analyse Brazilian O&amp;G policy decisions, based on historical data and Markov Chains. The model aims to support E&amp;P area offers, taking into account the consequences of the demand for E&amp;P goods and services. We apply the proposed model to Brazilian Bid Round contracts, and derive the optimal interval between Bid Rounds for SBs. The results suggest that the upstream sector in Brazil is impaired by the absence of a regular schedule for Bid Rounds, and will be improved with more predictability in exploratory offers.</p

A two-phase time aggregation algorithm for average cost Markov decision processes

This paper introduces a two-phase approach to solve average cost Markov decision processes, which is based on state space embedding or time aggregation. In the first phase, time aggregation is applied for policy evaluation in a prescribed subset of the state space, and a novel result is applied to expand the evaluation to the whole state space. This evaluation is then used in the second phase in a policy improvement step, and the two phases are then sequentially applied until convergence is attained or a prescribed running time is exceeded.</p

Discounted Markov decision processes via time aggregation

This paper applies two-phase time aggregation to solve discounted Markov decision processes (MDP). This procedure, recently proposed for average cost MDPs, is extended here discounted MDPs with a view at easing the computational burden associated to finding a quality solution within a reasonable time frame. Numerical examples are presented to illustrate the results.</p

Time aggregated Markov decision processes via standard dynamic programming

This note addresses the time aggregation approach to ergodic finite state Markov decision processes with uncontrollable states. We propose the use of the time aggregation approach as an intermediate step toward constructing a transformed MDP whose state space is comprised solely of the controllable states. The proposed approach simplifies the iterative search for the optimal solution by eliminating the need to define an equivalent parametric function, and results in a problem that can be solved by simpler, standard MDP algorithms.</p

Standard dynamic programming applied to time aggregated Markov decision processes

In this note we address the time aggregation approach to ergodic finite state Markov decision processes with uncontrollable states. We propose the use of the time aggregation approach as an intermediate step toward constructing a transformed MDP whose state space is comprised solely of the controllable states. The proposed approach simplifies the iterative search for the optimal solution by eliminating the need to define an equivalent parametric function, and results in a problem that can be solved by simpler, standard MDP algorithms.</p