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- A Cellular Automata Model of Spatio-Temporal Distribution of SpeciesPublication . Bioco, João; Silva, João; Canovas, Fernando; Fazendeiro, PauloCellular automata (CA) are discrete models frequently used in ecological and epidemiological studies due to the capacity to simulate dynamics systems and analyze their behavior. One of the applications of CA in ecology is in the analysis of the spatial distribution of species, where models are created and simulated in order to study the response of ecological systems to different kinds of exogenous or endogenous perturbations. In this study we describe an implementation of a cellular automaton model able to incorporate environmental data collected from different heterogeneous sources. To the user is given the power to produce and analyze different scenarios by combining the available variables at will. Different hypothesis regarding the individual contribution of each environmental variable can be promptly tested. As an illustrative example of the flexibility of our implementation we present a case study where, departing from a general additive model (GAM), validated in the literature, a possible explanation is given for the spatio-temporal distribution of two haplotypes of honeybees along Iberian Peninsula. Environmental data were used to describe every 30x30 second unit grid of the study area (World Geodetic System 1984 WGS84, geographical coordinates). The results of our model are compared and discussed at the light of the real data collected on the terrain. Curiously enough, both in the synthesized model and in the real data, one can observe that the frequency of African haplotypes decreases in a SW-NE trend, while that of west European lineage increases.
- Suitability of Agent-Based Models to Predict Spatio-temporal Distribution of SpeciesPublication . Bioco, João Holder Dulo; Fazendeiro, Paulo André Pais; Garcia, Fernando CánovasSpecies distribution models are used to describe the species-environment relationship. These models are widely applied in ecological and environmental modelling mainly to analyse the causes and effects of climate changes in the ecosystem. Climate changes contribute significantly to several observed phenomena among which stand out the displacement of species from their natural habitat, the colonization of invasive species, and even the extinction of species, for instance. Mechanisms that allow analysing and predicting such phenomena are widely needed in order to adopt measures that ensure the management, conservation and preservation of biodiversity. Despite ensuring the projection of the species distribution in the environment, species distribution models have limitations concerning representing the species’ behaviour in this projected environment. In generic terms, there is a set of useful information regarding the species’ life cycle that is not taken into account, resulting in predictions less specific concerning the species’ reaction to the environmental stimulus. To address these limitations, agent-based models approaches have been successfully adopted. Normally, these agent-based models are composed of individuals that incorporate simple behavioural rules. Therefore, the interaction between them, observed in an abstraction of the environment, could allow the description of complex systems offering a more reliable prediction regarding the species’ behaviour in the environment. In this work we propose a model resulting from the combination of traditional species distribution models with the agent-based models approach to ensure better characterisation of the species-environment relationship. Usually, agent-based models implementations are quite time-consuming and can demand a lot of computer resources. To minimize the computational cost resulting from the models’ simulation, we presented a parallelization strategy that allows increased speedups, and at the same time ensures the integrity of the results. Another challenge inherent in implementing agent-based models concerns the measurement of the time scale, i.e., mapping between computational and geological time. We can easily identify the computational time of a simulation; however, when it comes to establishing a mapping in real time, difficulties are increased. In our attempt to map the computational time with the geological time, we developed a method capable of estimating the geological time of a simulation for our agent-based models. This method also allowed performing predictions of species distribution in dynamic environments. Much of the lessons learned from this study as well as our approach concerning the species distribution simulation, were integrated into an open-access computational tool.
- Towards Forest Fire Prevention and Combat Through Citizen SciencePublication . Bioco, João; Fazendeiro, PauloInvolving the community (volunteers) in citizen science projects is a good way to address and prevent a lot of societal concerns. The participation of volunteers has been quit frequent in citizen science projects; making them a fundamental key for the success of these projects. Volunteers participate in citizen science projects by collecting and processing data that can be used for various purposes such as educational, scientific, preservation of biodiversity, decision-making, etc. In forest fire prevention, participation of citizens in collecting and processing data could help significantly in decision-making related to forest fire prevention and mitigation. Mobile phones can be the tool of choice for collecting data due to not only to its wide availability and powerful communication features but also to its embedded sensing capabilities such as GPS location, camera and microphone. This study is concerning to the development of a mobile-based citizen science project that allows volunteers to report fire-prone area, the occurrence of fire, and area where fire has occurred; then these information are used by firefighters and specialists for decision-making. In a scenario of fire occurrence, volunteers can take a picture of the place where the fire is occurring, upload to the mobile application, and send GPS location of the place; then the application notifies the firefighters and helps them allocate the needed resources to combat the fire based on the information sent by the volunteers.
- Parameterization of an Agent-Based Model of Spatial Distribution of SpeciesPublication . Bioco, João; Fazendeiro, Paulo; Canovas, Fernando; Prata, PaulaAgent-based models (ABMs) have been widely applied in several fields such as ecology, biology, climate changes, engineering and many other fields. In ABMs approach, the behaviour of a system is determined by the local interactions between its individuals (agents), and the interactions between these individuals with the environment where they exist. Due to its interactions at the individual’s level, ABMs can produce quite realistic results regarding to the models behaviour. Therefore it is necessary to perform several analysis from the point of view of the models parametrization. In this paper we perform a parametric study in ways to analyze the implications of models parameterization in the models output, by implementing an agent-based model to simulate spatial distribution of species in an heterogeneous environment. The models output resulting from several parameters combination are compared and discussed.
- Remarks on the Behavior of an Agent-Based Model of Spatial Distribution of SpeciesPublication . Bioco, João; Prata, Paula; Cánovas, Fernando; Fazendeiro, PauloAgent-based models have gained considerable notoriety in ecological modeling as well as in several other fields yearning for the ability to capture the emergent behavior of a complex system in which individuals interact with each other and with their environment. These models are implemented by applying a bottom-up approach, where the entire behavior of the system emerges from the local interaction between their components (agents or individuals). Usually, these interactions between individuals and their enclosing environment are modeled by very simple local rules. From the conceptual point of view, another appealing characteristic of this simulation approach is that it is well aligned with the reality whenever the system is composed of a multitude of individuals (behavioral units) that can be flexibly combined and placed in the environment. Due to their inherent flexibility, and despite of their simplicity, it is necessary to pay attention to the adjustments in their parameters which may result in unforeseen changes on the overall behavior of these models. In this paper we study the behavior of an agent-based model of spatial distribution of species, by analyzing the effects of the model parameters and the implications of the environment variables (that compose the environment where the species lives) on the models’ output. The presented experiments show that the behavior of the model depends mainly on the conditions of the environment where the species live, and the main parameters presented in life cycle of the species.
- SDSim: A generalized user friendly web ABM system to simulate spatiotemporal distribution of species under environmental scenariosPublication . Bioco, João; Cánovas, Fernando; Prata, Paula; Fazendeiro, PauloThis paper presents the Agent-Based Modelling System of spatial distribution of species SDSim. SDSim is an agent-based modelling system designed to simulate spatial distribution of species and populations for conservation and management purposes. SDSim gives to modellers the ability to simulate movements and colonization patterns of species given locations under study and a set of eco-geographical variables in which species depends on.
- Synchronization Overlap Trade-Off for a Model of Spatial Distribution of SpeciesPublication . Bioco, João; Prata, Paula; Cánovas, Fernando; Fazendeiro, PauloDespite of the widespread implementation of agent-based models in ecological modeling and another several areas, modelers have been concerned by the time consuming of these type of models. This paper presents a strategy to parallelize an agent-based model of spatial distribution of biological species, operating in a multi-stage synchronous distributed memory mode, as a way to obtain gains in the performance while reducing the need for synchronization. A multiprocessing implementation divides the environment (a rectangular grid corresponding to the study area) into stage-subsets, according to the number of defined or available processes. In order to ensure that there is no information loss, each stage-subset is extended with an overlapping section from each one of its neighbouring stage-subsets. The effect of the size of this overlapping on the quality of the simulations is studied. These results seem to indicate that it is possible to establish an optimal trade-off between the level of redundancy and the synchronization frequency. The reported paralellization method was tested in a standalone multicore machine but may be seamlessly scalable to a computation cluster.
- On the Modelling of Species Distribution: Logistic Regression Versus Density Probability FunctionPublication . Bioco, João; Prata, Paula; Canovas, Fernando; Fazendeiro, PauloThe concerns related to climate changes have been gaining attention in the last few years due to the negative impacts on the environment, economy, and society. To better understand and anticipate the effects of climate changes in the distribution of species, several techniques have been adopted comprising models of different complexity. In general, these models apply algorithms and statistical methods capable of predicting in a particular study area, the locations considered suitable for a species to survive and reproduce, given a set of eco-geographical variables that influence species behavior. Logistic regression algorithm and Probability density function are two common methods that can be used to model the species suitability. The former is a representative of a class of models that requires the availability (or imputation) of presence-absence data whereas the latter represents the models that only require presence data. Both approaches are compared regarding the capability to accurately predict the environmental suitability for species. On a different way, the behaviour of the species in the projected environments are analysed by simulating its potential distribution in the projected environment. A case study reporting results from two types of species with economical interest is presented: the strawberry tree (Arbutus unedo) in mainland Portugal, and the Apis mellifera (African Lineage) in the Iberian Peninsula.
- Prediction of the Arbutus unedo colonization time via an agent-based distribution modelPublication . Bioco, João; Prata, Paula; Canovas, Fernando; Fazendeiro, PauloSpecies distribution models (SDMs) have been used to predict the distribution of species in an environment. Usually, this prediction is based on previous species occurrence data. Nowadays, several species are facing climatic changes that impact the way species behave. Global warming has implicated species displacement from their natural habitat to new suitable places. SDMs can be used to anticipate possible impacts of the climatic changes in species distribution, preventing species extinction scenarios. New SDMs approaches are needed to give valuable information that better approximates these models to reality. This paper presents a novel approach to the prediction of species distribution. It starts by using an agent-based model (ABM) to establish an approximate mapping between the geological and the computational times. Afterwards, the implemented ABM can be used to predict the species distribution at different time intervals. The presented case study concerns the distribution of the Arbutus unedo in the Iberian peninsula, a species with relevant socio-economic impact in Portugal. The results show that the measurement of the geological time, supported by the approximate correspondence to the number of epochs of an ABM, can be a valuable tool for the prediction of the species distribution in a changing environmental scenario.