M. Angels Colomer

Man‐induced activities modify demographic parameters in a long‐lived species: effects of poisoning and health policies

Recent changes in sanitary policies within the European Union (EU) concerning disposal of carcasses of domestic animals and the increase of non-natural mortality factors, such as illegal poisoning, are threatening European vultures. However, the effects of anthropogenic activities on demographic parameters are poorly studied. Using a long-term study (1994-2011) of the threatened Pyrenean Bearded Vulture Gypaetus barbatus population, we assess the variation in the proportion of breeding pairs, egg-laying dates, clutch size, breeding success, and survival following a sharp reduction in food availability in 2005 due to the application of restrictive sanitary policies decreasing livestock carcass availability. We found a delay in laying dates and a regressive trend in clutch size, breeding success, and survival following policy change. The maintenance of specific supplementary feeding stations for Bearded Vultures probably reduced the negative effects of illegal poisoning and food shortages, which mainly affected subadult survival. A drop in food availability may have produced changes in demographic parameters and an increase in mortality due to an increased exposure to contaminated food. As a result, supplementary feeding as a precautionary measure can be a useful tool to reduce illegal poisoning and declines in demographic parameters until previous food availability scenarios are achieved. This study shows how anthropogenic activities through human health regulations that affect habitat quality can suddenly modify demographic parameters in long-lived species, including those, such as survival, with high sensitivity to population growth rate.

A computational modeling for real ecosystems based on P systems

In this paper, a P systems based general framework for modeling ecosystems dynamics is presented. Particularly, ecosystems are specified by means of multienvironment P systems composed of a finite number of environments, each of them having an extended P system with active membranes. The semantics is of a probabilistic type and it is implemented by assigning each rule of the system a probabilistic constant which depends on the environment and the run time. As a case study, two real ecosystems are described: scavenger birds in the Catalan Pyrenees and the zebra mussel (Dreissena Polymorpha) in Ribarroja reservoir (Spain).

Modeling Ecosystems Using P Systems: The Bearded Vulture, a Case Study

The Bearded Vulture (Gypaetus barbatus ) is an endangered species in Europe that feeds almost exclusively on bone remains of wild and domestic ungulates. In this paper, we present a model of an ecosystem related to the Bearded Vulture in the Pyrenees (NE Spain), by using P systems. The evolution of six species is studied: the Bearded Vulture and five subfamilies of domestic and wild ungulates upon which the vulture feeds. P systems provide a high level computational modeling framework which integrates the structural and dynamic aspects of ecosystems in a comprehensive and relevant way. P systems explicitly represent the discrete character of the components of an ecosystem by using rewriting rules on multisets of objects which represent individuals of the population and bones. The inherent stochasticity and uncertainty in ecosystems is captured by using probabilistic strategies. In order to experimentally validate the P system designed, we have constructed a simulator that allows us to analyze the evolution of the ecosystem under different initial conditions.

A p system based model of an ecosystem of some scavenger birds

In [1], we presented a P system in order to study the evolution of the bearded vulture in the Pyrenees (NE Spain). Here, we present a new model that overcomes some limitations of the previous work incorporating other scavenger species and additional prey species that provide food for the scavenger intraguild and interact with the Bearded Vulture in the ecosystem. After the validation, the new model can be a useful tool for the study of the evolution and management of the ecosystem. P systems provide a high level computational modelling framework which integrates the structural and dynamical aspects of ecosystems in a compressive and relevant way. The inherent randomness and uncertainty in ecosystems is captured by using probabilistic strategies.

Comparing simulation algorithms for multienvironment probabilistic P systems over a standard virtual ecosystem

Membrane Computing has recently proved to be a suitable framework for addressing the modelling of dynamical biological systems in general, and ecosystems in particular. Due to the inherent randomness and uncertainty in biological systems, when designing a model the relevant tasks to be addressed are the validation and virtual experimentation processes, rather than the formal verification. It is therefore crucial to rely on software implementations of efficient simulation algorithms. This paper presents a simple (but realistic enough) ecosystem where a carnivore and several herbivorous species interact. The model of this ecosystem has been used to compare experimentally the performance of two different simulation algorithms.

A new simulation algorithm for multienvironment probabilistic P systems

Multienvironment P systems are the base of a general framework for modeling ecosystems dynamics. On one hand, this modeling framework represents the structural and dynamical aspects of real ecosystems in a discrete, modular and compressive way. On the other hand, the inherent randomness and uncertainty of biological systems are captured by using probabilistic strategies. Nowadays, the simulation of these P systems based models is fundamental for experimentation and validation. In this paper, we introduce a new simulation algorithm, called DNDP, which performs object distribution and maximal consistency in the application of rules, that are crucial aspects of these systems. The paper also depicts a parallel implementation of the algorithm, and a comparison with the existing algorithm in PLinguaCore is provided. In order to test the performance of the presented algorithm, several experiments (simulations) have been carried out over four simple P systems with the same skeleton and different number of environments.

A uniform framework for modeling based on P systems

In this paper, a P systems based general framework for modeling the dynamics of a population biology is presented. Multienvironment probabilistic functional P systems with active membranes provide the syntactical specification, and the semantics is captured by using stochastic or probabilistic strategies implemented through simulation algorithms.

Handling markov chains with membrane computing

In this paper we approach the problem of computing the n–th power of the transition matrix of an arbitrary Markov chain through membrane computing. The proposed solution is described in a semi–uniform way in the framework of P systems with external output. The amount of resources required in the construction is polynomial in the number of states of the Markov chain and in the power. The time of execution is linear in the power and is independent of the number of states involved in the Markov chain.

Mistake index as a surrogate of quality in scientific manuscripts

Siler et al. (1) argue that “editors and peer reviewers generally—but not always—make good decisions regarding the identification and promotion of quality in scientific manuscripts.” Here, we provide a new perspective on the ongoing discussion regarding the quality of peer review and editing processes and discuss how the effectiveness of editors and peer reviewers can be measured. As well, we use a tool to analyze temporal trends in the effectiveness of the editing process and propose the use of a standardized corrigendum/erratum index as a surrogate of quality in scientific …

PSysCal: a parallel tool for calibration of ecosystem models

The methods used for ecosystem modelling are generally based on differential equations. Nowadays, new computational models based on concurrent processing of multiple agents (multi-agents) or the simulation of biological processes with the Population Dynamic P-System models (PDPs) are gaining importance. These models have significant advantages over traditional models, such as high computational efficiency, modularity and its ability to model the interaction between different biological processes which operate concurrently. By this, they are becoming useful for simulating complex dynamic ecosystems, untreatable with classical techniques.On the other hand, the main counterpart of P-System models is the need for calibration. The model parameters represent the field measurements taken by experts. However, the exact values of some of these parameters are unknown and experts define a numerical interval of possible values. Therefore, it is necessary to perform a calibration process to fit the best value of each interval. When the number of unknown parameters increases, the calibration process becomes computationally complex and storage requirements increase significantly.In this paper, we present a parallel tool (PSysCal) for calibrating next generation PDP models. The results shown that the calibration time is reduced exponentially with the amount of computational resources. However, the complexity of the calibration process and a limitation in the number of available computational resources make the calibration process intractable for large models. To solve this, we propose a heuristic technique (PSysCal+H). The results show that this technique significantly reduces the computational cost, it being practical for solving large model instances even with limited computational resources.