Predicting Stream Nitrogen Concentration From Watershed Features Using Neural Networks

The present work describes the development and validation of an artificial neural network (ANN) for the purpose of estimating inorganic and total nitrogen concentrations. The ANN approach has been developed and tested using 927 nonpoint source watersheds studied for relationships between macro-drainage area characteristics and nutrient levels in streams. The ANN had eight independent input variables of watershed parameters (five on land use features, mean annual precipitation, animal unit density and mean stream flow) and two dependent output variables (total and inorganic nitrogen concentrations in the stream). The predictive quality of ANN models was judged with “hold-out” validation procedures. After ANN learning with the training set of data, we obtained a correlation coefficient r of about 0.85 in the testing set. Thus, ANNs are capable of learning the relationships between drainage area characteristics and nitrogen levels in streams, and show a high ability to predict from the new data set. On the basis of the sensitivity analyses we established the relationship between nitrogen concentration and the eight environmental variables

SimpactCyan 1.0 : an open-source simulator for individual-based models in HIV epidemiology with R and Python interfaces

SimpactCyan is an open-source simulator for individual-based models in HIV epidemiology. Its core algorithm is written in C++ for computational efficiency, while the R and Python interfaces aim to make the tool accessible to the fast-growing community of R and Python users. Transmission, treatment and prevention of HIV infections in dynamic sexual networks are simulated by discrete events. A generic “intervention” event allows model parameters to be changed over time, and can be used to model medical and behavioural HIV prevention programmes. First, we describe a more efficient variant of the modified Next Reaction Method that drives our continuous-time simulator. Next, we outline key built-in features and assumptions of individual-based models formulated in SimpactCyan, and provide code snippets for how to formulate, execute and analyse models in SimpactCyan through its R and Python interfaces. Lastly, we give two examples of applications in HIV epidemiology: the first demonstrates how the software can be used to estimate the impact of progressive changes to the eligibility criteria for HIV treatment on HIV incidence. The second example illustrates the use of SimpactCyan as a data-generating tool for assessing the performance of a phylodynamic inference framework

Modelling forest landscape dynamics in Glen Affric, northern Scotland

Consideration of forest management at the landscape scale is essential if commitments to the conservation of biodiversity are to be upheld. The ecosystem management approach, developed largely in North America, has made use of various landscape modelling tools to assist in planning for biodiversity maintenance and ecological restoration. The roles of habitat suitability models, metapopulation models, spatially explicit population models (SEPMs) and forest landscape dynamics models (FLDMs) in the planning process are discussed and a review of forest dynamics models is presented. Potential is identified for developing landscape models in the UK for both landscape restoration projects and semi-natural woodland management. Glen Affric, in northern Scotland contains a large area of native pine and birch woodland and is the subject of a long-term restoration project. A new model, GALDR (Glen Affric Landscape Dynamics Reconstruction) is introduced and is believed to be the first FLDM developed for British woodland. The theory behind the model is described in detail and preliminary results and sensitivity analyses are presented. Furthermore, GALAM (Glen Affric Lichen Abundance Model), a new SEPM for the rare epiphytic lichen Bryoria furcellata is also described. Results of simulations from the linked GALDR and GALAM models are presented which shed light on the role of landscape heterogeneity in determining the dynamics of lichen habitats and populations. It is concluded that, whilst much work will be required to develop a management-oriented decision support system from the GALDR model, the modelling process may aid researchers in the identification of knowledge gaps in ecological theory relevant to management and restoration

Ecological models at fish community and species level to support effective river restoration

RESUMEN Los peces nativos son indicadores de la salud de los ecosistemas acuáticos, y se han convertido en un elemento de calidad clave para evaluar el estado ecológico de los ríos. La comprensión de los factores que afectan a las especies nativas de peces es importante para la gestión y conservación de los ecosistemas acuáticos. El objetivo general de esta tesis es analizar las relaciones entre variables biológicas y de hábitat (incluyendo la conectividad) a través de una variedad de escalas espaciales en los ríos Mediterráneos, con el desarrollo de herramientas de modelación para apoyar la toma de decisiones en la restauración de ríos. Esta tesis se compone de cuatro artículos. El primero tiene como objetivos modelar la relación entre un conjunto de variables ambientales y la riqueza de especies nativas (NFSR), y evaluar la eficacia de potenciales acciones de restauración para mejorar la NFSR en la cuenca del río Júcar. Para ello se aplicó un enfoque de modelación de red neuronal artificial (ANN), utilizando en la fase de entrenamiento el algoritmo Levenberg-Marquardt. Se aplicó el método de las derivadas parciales para determinar la importancia relativa de las variables ambientales. Según los resultados, el modelo de ANN combina variables que describen la calidad de ribera, la calidad del agua y el hábitat físico, y ayudó a identificar los principales factores que condicionan el patrón de distribución de la NFSR en los ríos Mediterráneos. En la segunda parte del estudio, el modelo fue utilizado para evaluar la eficacia de dos acciones de restauración en el río Júcar: la eliminación de dos azudes abandonados, con el consiguiente incremento de la proporción de corrientes. Estas simulaciones indican que la riqueza aumenta con el incremento de la longitud libre de barreras artificiales y la proporción del mesohabitat de corriente, y demostró la utilidad de las ANN como una poderosa herramienta para apoyar la toma de decisiones en el manejo y restauración ecológica de los ríos Mediterráneos. El segundo artículo tiene como objetivo determinar la importancia relativa de los dos principales factores que controlan la reducción de la riqueza de peces (NFSR), es decir, las interacciones entre las especies acuáticas, variables del hábitat (incluyendo la conectividad fluvial) y biológicas (incluidas las especies invasoras) en los ríos Júcar, Cabriel y Turia. Con este fin, tres modelos de ANN fueron analizados: el primero fue construido solamente con variables biológicas, el segundo se construyó únicamente con variables de hábitat y el tercero con la combinación de estos dos grupos de variables. Los resultados muestran que las variables de hábitat son los ¿drivers¿ más importantes para la distribución de NFSR, y demuestran la importancia ecológica de los modelos desarrollados. Los resultados de este estudio destacan la necesidad de proponer medidas de mitigación relacionadas con la mejora del hábitat (incluyendo la variabilidad de caudales en el río) como medida para conservar y restaurar los ríos Mediterráneos. El tercer artículo busca comparar la fiabilidad y relevancia ecológica de dos modelos predictivos de NFSR, basados en redes neuronales artificiales (ANN) y random forests (RF). La relevancia de las variables seleccionadas por cada modelo se evaluó a partir del conocimiento ecológico y apoyado por otras investigaciones. Los dos modelos fueron desarrollados utilizando validación cruzada k-fold y su desempeño fue evaluado a través de tres índices: el coeficiente de determinación (R2 ), el error cuadrático medio (MSE) y el coeficiente de determinación ajustado (R2 adj). Según los resultados, RF obtuvo el mejor desempeño en entrenamiento. Pero, el procedimiento de validación cruzada reveló que ambas técnicas generaron resultados similares (R2 = 68% para RF y R2 = 66% para ANN). La comparación de diferentes métodos de machine learning es muy útil para el análisis crítico de los resultados obtenidos a través de los modelos. El cuarto artículo tiene como objetivo evaluar la capacidad de las ANN para identificar los factores que afectan a la densidad y la presencia/ausencia de Luciobarbus guiraonis en la demarcación hidrográfica del Júcar. Se utilizó una red neuronal artificial multicapa de tipo feedforward (ANN) para representar relaciones no lineales entre descriptores de L. guiraonis con variables biológicas y de hábitat. El poder predictivo de los modelos se evaluó con base en el índice Kappa (k), la proporción de casos correctamente clasificados (CCI) y el área bajo la curva (AUC) característica operativa del receptor (ROC). La presencia/ausencia de L. guiraonis fue bien predicha por el modelo ANN (CCI = 87%, AUC = 0.85 y k = 0.66). La predicción de la densidad fue moderada (CCI = 62%, AUC = 0.71 y k = 0.43). Las variables más importantes que describen la presencia/ausencia fueron: radiación solar, área de drenaje y la proporción de especies exóticas de peces con un peso relativo del 27.8%, 24.53% y 13.60% respectivamente. En el modelo de densidad, las variables más importantes fueron el coeficiente de variación de los caudales medios anuales con una importancia relativa del 50.5% y la proporción de especies exóticas de peces con el 24.4%. Los modelos proporcionan información importante acerca de la relación de L. guiraonis con variables bióticas y de hábitat, este nuevo conocimiento podría utilizarse para apoyar futuros estudios y para contribuir en la toma de decisiones para la conservación y manejo de especies en los en los ríos Júcar, Cabriel y Turia.Olaya Marín, EJ. (2013). Ecological models at fish community and species level to support effective river restoration [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/28853TESI

Integration in the European Research Area by means of the European Framework Programmes. Findings from Eigenvector filtered spatial interaction models

One of the main goals of the European Research Area (ERA) concept is to improve coherence and integration across the European research landscape by removing barriers for collaborative knowledge production in a European system of innovation. The cornerstone of policy instruments in this context is the European Framework Programme (FP) that supports pre-competitive collaborative R&D projects, creating a pan-European network of actors performing joint R&D. However, we know only little about the contribution of the FPs to the realisation of ERA. The objective of this study is to monitor progress towards ERA by identifying the evolution of separation effects, such as spatial, institutional, cultural or technological barriers, which influence cross-region R&D collaboration intensities between 255 European NUTS-2 regions in the FPs over the time period 1999-2006. By this, the study builds on recent work by Scherngell and Barber (2009) that addresses this question from a static perspective. We employ Poisson spatial interaction models taking into account spatial autocorrelation among residual flows by using Eigenvector spatial filtering methods. The results show that geographical distance and country border effects gradually decrease over time when correcting for spatial autocorrelation among flows. Thus, the study provides evidence for the contribution of the FPs to the realisation of ERA.

Community Assembly and Habitat Specialization of Tropical Tree Species along Moisture Gradients in the Western Ghats Biodiversity Hotspot in India

The interactions between ecological and evolutionary processes mediated through functional traits that confer habitat specialization have been proposed to explain the spatial assembly of plant communities both across space and in different habitats. However, the scale at which these mechanisms operate and their relative importance in dominance and assembly of tree communities in different habitat types distributed across spatially-varying environmental gradients in tropical forests have been rarely tested. Here, I elucidate patterns of functional trait and phylogenetic variation and evolutionary history of key functional traits conferring habitat specialization to understand community assembly mechanisms operating within in tropical tree communities distributed across spatially varying environmental gradients and in different habitat types in Western Ghats biodiversity hotspot, India. The chapter 2 focuses on patterns of functional trait and phylogenetic co-variation among a community of tropical canopy trees distributed across spatially varying moisture gradient. I find that tree communities in plots that experience lower precipitation and longer duration of dry period show clustering of both functional traits and phylogenetic relationship suggesting environmental filtering play a key role in the assembly of tree communities in these forests. The chapter 3 explores the relationship between key functional traits, phylogenetic relationship and abundance of 210 co-occurring tree species distributed across contrasting extremes of seasonal flooding gradient i.e. flooded forest and terra-firme forest (non-flooded). I found that repeated evolution of key functional traits together with strong environmental filtering play a key role in determining the ecological success (dominance) and assembly of tree communities in flooded habitat. The chapter 4 focuses on climatic niche evolution and evolutionary history of flooded habitat specialization in global and endemic Myristicaceae members in the Western Ghats. I found that, repeated gain of swamp habitat specialization and associated morphological traits in global and Western Ghats Myristicaceae implying seasonal flooding gradient is an important driver of ecological speciation. I also found that local habitat specialization promotes range-wide niche evolution among sister taxa. By elucidating the pattern functional traits and phylogenetic relationship across flooding and spatially varying moisture gradient and analysis of climatic niche evolution and habitat specialization among co-occurring sister taxa, this thesis contributes to our understanding of the determinants of assembly, dominance and diversification of tropical tree communities across diverse habitat types in tropical forest biomes