Can multilayer perceptron ensembles model the ecological niche of freshwater fish species?

The potential of Multilayer Perceptron (MLP) Ensembles to explore the ecology of freshwater fish specieswas tested by applying the technique to redfin barbel (Barbus haasi Mertens, 1925), an endemic and mon-tane species that inhabits the North-East quadrant of the Iberian Peninsula. Two different MLP Ensembleswere developed. The physical habitat model considered only abiotic variables, whereas the biotic modelalso included the density of the accompanying fish species and several invertebrate predictors. The results showed that MLP Ensembles may outperform single MLPs. Moreover, active selection of MLP candidatesto create an optimal subset of MLPs can further improve model performance. The physical habitat modelconfirmed the redfin barbel preference for middle-to-upper river segments whereas the importance ofdepth confirms that redfin barbel prefers pool-type habitats. Although the biotic model showed higheruncertainty, it suggested that redfin barbel, European eel and the considered cyprinid species have similarhabitat requirements. Due to its high predictive performance and its ability to deal with model uncertainty, the MLP Ensemble is a promising tool for ecological modelling or habitat suitability prediction in environmental flow assessment.This study was funded by the Spanish Ministry of Economy and Competitiveness with the project SCARCE (Consolider-Ingenio 2010 CSD2009-00065) and the Universitat Politecnica de Valencia, through the project UPPTE/2012/294 (PAID-06-12). Additionally, the authors would like to thank the help of the Conselleria de Territori i Vivenda (Generalitat Valenciana) and the Confederacion Hidrografica del Jucar (Spanish government) which provided environmental data. The authors are indebted to all the colleagues who collaborated in the field data collection and the text adequacy; without their help this paper would have not been possible. Last but not least, the authors would like to specifically thank E. Aparicio and A.J. Cannon, the former because he selflessly provided the bibliography about the redfin barbel and the latter because he patiently explained the 'ins and outs' of the monmlp package.Muñoz Mas, R.; Martinez-Capel, F.; Alcaraz-Hernández, JD.; Mouton, AM. (2015). Can multilayer perceptron ensembles model the ecological niche of freshwater fish species?. Ecological Modelling. 309-310:72-81. https://doi.org/10.1016/j.ecolmodel.2015.04.025S7281309-31

Cascaded neural networks improving fish species prediction accuracy: the role of the biotic information

Species distribution is the result of complex interactions that involve environmental parameters as well as biotic factors. However, methodological approaches that consider the use of biotic variables during the prediction process are still largely lacking. Here, a cascaded Artifcial Neural Networks (ANN) approach is proposed in order to increase the accuracy of fsh species occurrence estimates and a case study for Leucos aulain NE Italy is presented as a demonstration case. Potentially useful biotic information (i.e. occurrence of other species) was selected by means of tetrachoric correlation analysis and on the basis of the improvements it allowed to obtain relative to models based on environmental variables only. The prediction accuracy of the L. aulamodel based on environmental variables only was improved by the addition of occurrence data for A. arborellaand S. erythrophthalmus. While biotic information was needed to train the ANNs, the fnal cascaded ANN model was able to predict L. aula better than a conventional ANN using environmental variables only as inputs. Results highlighted that biotic information provided by occurrence estimates for non-target species whose distribution can be more easily and accurately modeled may play a very useful role, providing additional predictive variables to target species distribution models

Predicting Fishing Footprint of Trawlers From Environmental and Fleet Data: An Application of Artificial Neural Networks

The increasing use of tracking devices, such as the Vessel Monitoring System (VMS) and the Automatic Identification System (AIS), have allowed, in the last decade, detailed spatial and temporal analyses of fishing footprints and of their effects on environments and resources. Nevertheless, tracking devices usually allow monitoring of the largest length classes composing different fleets, whereas fishing vessels below a regulatory threshold (i.e., 15 m in length-over-all) are not mandatorily equipped with these tools. This issue is critical, since 36% of the vessels in the European Union (EU) fleets belong to these “hidden” length classes. In this study, a model [namely, a cascaded multilayer perceptron network (CMPN)] is devised to predict the annual fishing footprints of vessels without tracking devices. This model uses information about fleet structures, environmental characteristics, human activities, and fishing effort patterns of vessels equipped with tracking devices. Furthermore, the model is able to take into account the interactions between different components of the fleets (e.g., fleet segments), which are characterized by different operating ranges and compete for the same marine space. The model shows good predictive performance and allows the extension of spatial analyses of fishing footprints to the relevant, although still unexplored, fleet segments

Recorded and potential alien invertebrate pests in Finnish agriculture and horticulture

It is assumed that climate change will promote pest invasions and their establishment in new regions. We have updated the list of current alien invertebrate pest species in Finland and produced a list of potential new alien pests using a self-organizing map (SOM) that ranks species in terms of their risk of entry into Finland. The 76 pest species recorded included 66 insects, 5 nematodes, 2 mites and 3 slugs. Nearly half of the alien species appeared to have invaded Finland during the last 48 years. The SOM analysis is considered a viable tool for identification of potentially high-risk invasive pests from among the multitude of potential alien invaders, and represents a useful complement to local expert knowledge-based risk assessment of potentially invasive pests. Along with the comparisons with databases of current and potential pest species, SOM analysis suggests that in the changing climate, the habitats at greatest risk from exotic pests in Finland are horticultural: orchards, ornamental hardy-nursery stocks, landscape and ornamental tree nurseries, and greenhouses

ADAPTIVE PROCESSING ARCHITECTURE OF MULTISENSOR SIGNALS FOR LOW-IMPACT TREATMENTS OF PLANT DISEASES.

Intelligent sensing for production of high-value crops Scientific and technical quality This thesis has been realized within the CROPS project. CROPS will develop scientific know-how for a highly configurable, modular and clever carrier platform that includes modular parallel manipulators and intelligent tools (sensors, algorithms, sprayers, grippers) that can be easily installed onto the carrier and are capable of adapting to new tasks and conditions. Several technological demonstrators will be developed for high value crops like greenhouse vegetables, fruits in orchards, and grapes for premium wines. The CROPS robotic platform will be capable of site-specific spraying (targets spray only towards foliage and selective targets) and selective harvesting of fruit (detects the fruit, determines its ripeness, moves towards the fruit, grasps it and softly detaches it). Another objective of CROPS is to develop techniques for reliable detection and classification of obstacles and other objects to enable successful autonomous navigation and operation in plantations and forests. The agricultural and forestry applications share many research areas, primarily regarding sensing and learning capabilities. The project started in October 2010 and will run for 48 month. The aim of this thesis is to lay the foundations, suggesting the guidelines, of one task addressed by the CROPS project, in particular, the aim of this work is to study the application of a VIS-NIR imaging approach (intelligent sensing), based on a relatively simple algorithm, to detect symptoms of powdery mildew and downy mildew disease at early stages of infection (sustainable production of high-value crops). Also a preliminary work for botrytis detection will be shown. Concept and objectives Many site-specific agricultural and forestry tasks, such as cultivating, transplanting, spraying, trimming, selective harvesting, and transportation, could be performed more efficiently if carried out by robotic systems. However, to date, agriculture and forestry robots are still not available, partly due to the complex, and often contradictory, demands for developing such systems. On the one hand, agro-forestry robots must be of reasonable cost, but on the other, they must be able to deal with complex, dynamic, and partly changing tasks. Addressing problems such as continuously changing conditions (e.g., rain and illumination), high variability in both the products (size, and shape) and the environment (location and soil properties), the delicate nature of the products, and hostile environmental conditions (e.g. dust, dirt, extreme temperature and humidity) requires advanced sensing, manipulation, and control. Since it is impossible to model a-priori all environments and task conditions, the robot must be able to learn new tasks and new working conditions. The solution to these demands lies in a modular and configurable design that will keep costs to a minimum by applying a basic configuration to a range of agricultural applications. At least a 95% yield rate is necessary for economical feasibility of an agro-forestry robotic system. Objectives An objective of CROPS project is to develop an \u201cintelligent tools\u201d (sensors, algorithms, sprayers) that can easily be installed onto a modular and clever carrier platform. The CROPS robotic platform will be capable of site-specific spraying (targeted spraying only on foliage and selected targets). Research efforts To achieve the novel systems described above, we will focus on intelligent sensing of disease detection on crop canopy (investigating different types and/or multiple sensors with decision making models). Technology evaluation Technology evaluation of the developed systems will include the performance evaluation of the different components (e.g., capacities, success rates/misses). Progress beyond the state-of-the-art Despite the extensive research conducted to date in applying robots to a variety of agriculture and forestry tasks (e.g., transplanting, spraying, trimming, selective harvesting), limited operating efficiencies (speeds, success rates) and lack of economic justification have severely limited commercialization. The few commercial autonomous agriculture and forestry robots that are available on the market include a cow milking robot, a robot for cutting roses (RomboMatic), and various remote-controlled forest harvesters. These robots either have a low level of autonomy or are able to perform only simple operations in structured and static environments (e.g. dairy farms and plant breeding facilities). Developing capabilities for robots operating in unstructured outdoor environments or dealing with the highly variable objects that exist in agriculture and forestry is still open-ended, and one of CROPS aims is to address this problem. Current state-of-the-art Field trials have routinely shown that most crop damage due to diseases and pests can be efficiently controlled when treatments are applied timely and accurately by hand to susceptible targets (i.e., by intelligent spraying). Site-specific spraying targeted solely to trees and/or to infected areas can reduce pesticide use by 20\u201340%. An issue of relevance to targeted agriculture is the detection of diseases in field crops. Since such events often have a visual manifestation, state-of-the-art methods for achieving this goal include fluorescence imaging or the analysis of spectral reflectance in carefully selected spectral bands. While reports of these methods used separately achieved performance at 75\u201390% accuracy, attempts to combine them have boosted disease discrimination accuracy to 95%. We must note here, however, that despite these promising results, very little research has been conducted on in-field disease detection. Expected progress The diseased detection approach for precision pesticide spraying will be developed investigating image processing techniques (after a laboratory spectral evaluation and greenhouse testing) for high-precision close-range targeted spraying to selectively and precisely apply chemicals solely to targets susceptible to specific diseases/pests, with a mean 90% success rate. Local changes in spectral reflection of parts of the canopy will be used as an indication of disease. \u201cSoft-sensor\u201d for detection of ripeness and diseases (noncontact rapid sensing system) will be developed by multispectral sensor (multispectral spectral camera). These \u201csoft sensor\u201d can be used as a decision model for targeted spraying

Spatiotemporal Wireless Sensor Network Field Approximation with Multilayer Perceptron Artificial Neural Network Models

As sensors become increasingly compact and dependable in natural environments, spatially-distributed heterogeneous sensor network systems steadily become more pervasive. However, any environmental monitoring system must account for potential data loss due to a variety of natural and technological causes. Modeling a natural spatial region can be problematic due to spatial nonstationarities in environmental variables, and as particular regions may be subject to specific influences at different spatial scales. Relationships between processes within these regions are often ephemeral, so models designed to represent them cannot remain static. Integrating temporal factors into this model engenders further complexity. This dissertation evaluates the use of multilayer perceptron neural network models in the context of sensor networks as a possible solution to many of these problems given their data-driven nature, their representational flexibility and straightforward fitting process. The relative importance of parameters is determined via an adaptive backpropagation training process, which converges to a best-fit model for sensing platforms to validate collected data or approximate missing readings. As conditions evolve over time such that the model can no longer adapt to changes, new models are trained to replace the old. We demonstrate accuracy results for the MLP generally on par with those of spatial kriging, but able to integrate additional physical and temporal parameters, enabling its application to any region with a collection of available data streams. Potential uses of this model might be not only to approximate missing data in the sensor field, but also to flag potentially incorrect, unusual or atypical data returned by the sensor network. Given the potential for spatial heterogeneity in a monitored phenomenon, this dissertation further explores the benefits of partitioning a space and applying individual MLP models to these partitions. A system of neural models using both spatial and temporal parameters can be envisioned such that a spatiotemporal space partitioned by k-means is modeled by k neural models with internal weightings varying individually according to the dominant processes within the assigned region of each. Evaluated on simulated and real data on surface currents of theGulf ofMaine, partitioned models show significant improved results over single global models

Comparing Ensemble and Cascaded Neural Networks that Combine Biotic and Abiotic Variables to Predict Insect Species Distribution

Michael J. Watts and S. P. Worne

Machine learning approach to reconstructing signalling pathways and interaction networks in biology

In this doctoral thesis, I present my research into applying machine learning techniques for reconstructing species interaction networks in ecology, reconstructing molecular signalling pathways and gene regulatory networks in systems biology, and inferring parameters in ordinary differential equation (ODE) models of signalling pathways. Together, the methods I have developed for these applications demonstrate the usefulness of machine learning for reconstructing networks and inferring network parameters from data. The thesis consists of three parts. The first part is a detailed comparison of applying static Bayesian networks, relevance vector machines, and linear regression with L1 regularisation (LASSO) to the problem of reconstructing species interaction networks from species absence/presence data in ecology (Faisal et al., 2010). I describe how I generated data from a stochastic population model to test the different methods and how the simulation study led us to introduce spatial autocorrelation as an important covariate. I also show how we used the results of the simulation study to apply the methods to presence/absence data of bird species from the European Bird Atlas. The second part of the thesis describes a time-varying, non-homogeneous dynamic Bayesian network model for reconstructing signalling pathways and gene regulatory networks, based on L`ebre et al. (2010). I show how my work has extended this model to incorporate different types of hierarchical Bayesian information sharing priors and different coupling strategies among nodes in the network. The introduction of these priors reduces the inference uncertainty by putting a penalty on the number of structure changes among network segments separated by inferred changepoints (Dondelinger et al., 2010; Husmeier et al., 2010; Dondelinger et al., 2012b). Using both synthetic and real data, I demonstrate that using information sharing priors leads to a better reconstruction accuracy of the underlying gene regulatory networks, and I compare the different priors and coupling strategies. I show the results of applying the model to gene expression datasets from Drosophila melanogaster and Arabidopsis thaliana, as well as to a synthetic biology gene expression dataset from Saccharomyces cerevisiae. In each case, the underlying network is time-varying; for Drosophila melanogaster, as a consequence of measuring gene expression during different developmental stages; for Arabidopsis thaliana, as a consequence of measuring gene expression for circadian clock genes under different conditions; and for the synthetic biology dataset, as a consequence of changing the growth environment. I show that in addition to inferring sensible network structures, the model also successfully predicts the locations of changepoints. The third and final part of this thesis is concerned with parameter inference in ODE models of biological systems. This problem is of interest to systems biology researchers, as kinetic reaction parameters can often not be measured, or can only be estimated imprecisely from experimental data. Due to the cost of numerically solving the ODE system after each parameter adaptation, this is a computationally challenging problem. Gradient matching techniques circumvent this problem by directly fitting the derivatives of the ODE to the slope of an interpolant. I present an inference procedure for a model using nonparametric Bayesian statistics with Gaussian processes, based on Calderhead et al. (2008). I show that the new inference procedure improves on the original formulation in Calderhead et al. (2008) and I present the result of applying it to ODE models of predator-prey interactions, a circadian clock gene, a signal transduction pathway, and the JAK/STAT pathway