Investigation in the application of complex algorithms to recurrent generalized neural networks for modeling dynamic systems

Neural networks are mathematical formulations that can be "trained" to perform certain functions. One particular application of these networks of interest in this thesis is to "model" a physical system using only input-output information. The physical system and the neural network are subjected to the same inputs. The neural network is then trained to produce an output which is the same as the physical system for any input. This neural network model so created is essentially a "blackbox" representation of the physical system. This approach has been used at the University of Saskatchewan to model a load sensing pump (a component which is used to create a constant flow rate independent of variations in pressure downstream of the pump). These studies have shown the versatility of neural networks for modeling dynamic and non-linear systems; however, these studies also indicated challenges associated with the morphology of neural networks and the algorithms to train them. These challenges were the motivation for this particular research. Within the Fluid Power Research group at the University of Saskatchewan, a "global" objective of research in the area of load sensing pumps has been to apply dynamic neural networks (DNN) in the modeling of loads sensing systems.. To fulfill the global objective, recurrent generalized neural network (RGNN) morphology along with a non-gradient based training approach called the complex algorithm (CA) were chosen to train a load sensing pump neural network model. However, preliminary studies indicated that the combination of recurrent generalized neural networks and complex training proved ineffective for even second order single-input single-output (SISO) systems when the initial synaptic weights of the neural network were chosen at random. Because of initial findings the focus of this research and its objectives shifted towards understanding the capabilities and limitations of recurrent generalized neural networks and non-gradient training (specifically the complex algorithm). To do so a second-order transfer function was considered from which an approximate recurrent generalized neural network representation was obtained. The network was tested under a variety of initial weight intervals and the number of weights being optimized. A definite trend was noted in that as the initial values of the synaptic weights were set closer to the "exact" values calculated for the system, the robustness of the network and the chance of finding an acceptable solution increased. Two types of training signals were used in the study; step response and frequency based training. It was found that when step response and frequency based training were compared, step response training was shown to produce a more generalized network. Another objective of this study was to compare the use of the CA to a proven non-gradient training method; the method chosen was genetic algorithm (GA) training. For the purposes of the studies conducted two modifications were done to the GA found in the literature. The most significant change was the assurance that the error would never increase during the training of RGNNs using the GA. This led to a collapse of the population around a specific point and limited its ability to obtain an accurate RGNN. The results of the research performed produced four conclusions. First, the robustness of training RGNNs using the CA is dependent upon the initial population of weights. Second, when using GAs a specific algorithm must be chosen which will allow the calculation of new population weights to move freely but at the same time ensure a stable output from the RGNN. Third, when the GA used was compared to the CA, the CA produced more generalized RGNNs. And the fourth is based upon the results of training RGNNs using the CA and GA when step response and frequency based training data sets were used, networks trained using step response are more generalized in the majority of cases

Evaluation of the response and recovery of a forested watershed to human disturbances based on a multi-proxy analysis of sediments in Trout Pond, Lyme, NH

The New England landscape has undergone significant changes since the arrival of European settlers. Variations in the accumulation of allochthonous and autochthonous sediment in lakes provides a unique opportunity to evaluate the response and recovery of a lake and its watershed to human disturbances in comparison with long-term natural variability. Trout Pond is a small (0.06 km2), 13 m deep lake in Lyme, New Hampshire (43.83° N, 72.09° W). In the late 19th century a small logging camp was located near the pond and much of the 1.5 square km watershed was deforested. Currently protected by a conservation easement, the Trout Pond watershed has completely reforested and is free of any direct human impacts, making it an ideal site for examining the response and recovery of a forested watershed to human disturbances. A 1.45 m sediment cores, recovered from the deepest part of Trout Pond, was analyzed using a multi-proxy evaluation of the bulk density, organic carbon content, total nitrogen content, bulk δ13C, magnetic susceptibility, and diatom content of the sediments. A chronology, derived from 137Cs, bulk Pb, and two, radiocarbon dates, was used to evaluate the geochemical results over time. Results show a constant period of sedimentation for the first ~1800 years of the core. We attribute the onset of human landscape to changes in the watershed between 1820 and 1890 AD. These changes are evident in the geochemical and biological proxies, with sharp increases in bulk density, total organic carbon and nitrogen, more negative d13C values and increases in the centric to pennate ratio and total abundance of diatoms. Historical evidence of three, active sawmills in the Trout Pond watershed during this time period support our findings. Only in the past 60 years (~1950 AD- present) has there been a decrease in bulk mass, carbon and nitrogen accumulation rates to pre-disturbance values. While land use rapidly changed the landscape during the early 20th century from deforestation to re-growth, the land use signal recorded in the Trout Pond sediments show a lag period between reforestation and a decrease in bulk mass accumulation

Bioeconomic Model of Rainbow Trout (\u3cem\u3eOncorhynchus mykiss\u3c/em\u3e) and Humpback Chub (\u3cem\u3eGila cypha\u3c/em\u3e) Management in the Grand Canyon

The Colorado River, from Glen Canyon Dam (GCD) to the Little Colorado River (LCR) confluence, includes both non-native Rainbow Trout (Oncorhynchus mykiss) and endangered native Humpback Chub (Gila cypha). While both Rainbow Trout and Humpback Chub are valued fish species in this system, Rainbow Trout can have a negative effect on Humpback Chub survival. We developed a bioeconomic model to determine management actions that minimize the costs of controlling Rainbow Trout abundance subject to achieving Humpback Chub population goals. The model is compartmentalized into population and management components. The population component characterizes the stylized dynamics of Rainbow Trout and Humpback Chub from GCD to the LCR confluence within the Colorado River. The management component of the model identifies Rainbow Trout mechanical removal strategies that achieve average annual juvenile Humpback Chub survival targets while minimizing management costs. This research is an interdisciplinary effort combining biological models and economic methods to address federal, state and tribal stakeholder resource goals related to Rainbow Trout and Humpback Chub management in this complex social-ecological system

Remarkable Response of Native Fishes to Invasive Trout Suppression Varies With Trout Density, Temperature, and Annual Hydrology

Recovery of imperiled fishes can be achieved through suppression of invasives, but outcomes may vary with environmental conditions. We studied the response of imperiled desert fishes to an invasive brown (Salmo trutta) and rainbow trout (Oncorhynchus mykiss) suppression program in a Colorado River tributary, with natural flow and longitudinal variation in thermal characteristics. We investigated trends in fish populations related to suppression and tested hypotheses about the impacts of salmonid densities, hydrologic variation, and spatial–thermal gradients on the distribution and abundance of native fish species using zero-inflated generalized linear mixed effects models. Between 2012 and 2018, salmonids declined 89%, and native fishes increased dramatically (∼480%) once trout suppression surpassed ∼60%. Temperature and trout density were consistently retained in the top models predicting the abundance and distribution of native fishes. The greatest increases occurred in warmer reaches and in years with spring flooding. Surprisingly, given the evolution of native fishes in disturbance-prone systems, intense, monsoon-driven flooding limited native fish recruitment. Applied concertedly, invasive species suppression and efforts to mimic natural flow and thermal regimes may allow rapid and widespread native fish recovery

Exploring Metapopulation-Scale Suppression Alternatives for a Global Invader in a River Network Experiencing Climate Change

Invasive species can dramatically alter ecosystems, but eradication is difficult, and suppression is expensive once they are established. Uncertainties in the potential for expansion and impacts by an invader can lead to delayed and inadequate suppression, allowing for establishment. Metapopulation viability models can aid in planning strategies to improve responses to invaders and lessen invasive species’ impacts, which may be particularly important under climate change. We used a spatially-explicit metapopulation viability model to explore suppression strategies for ecologically-damaging invasive brown trout (Salmo trutta), established in the Colorado River and a tributary within Grand Canyon National Park. Our goals were to: 1) estimate the effectiveness of strategies targeting different life stages and subpopulations within a metapopulation, 2) quantify the effectiveness of a rapid response to a new invasion relative to delaying action until establishment; and 3) estimate whether future hydrology and temperature regimes related to climate change and reservoir management affect metapopulation viability and alter the optimal management response. We included scenarios targeting different life-stages with spatially-varying intensities of electrofishing, redd destruction, incentivized angler harvest, piscicides, and a weir. Quasi-extinction (QE) was obtainable only with metapopulation-wide suppression targeting multiple life-stages; subpopulations were most sensitive to age-0 and large adult mortality. The duration of suppression needed to reach QE for a large established subpopulation was triple compared to a rapid response to a new invasion. Isolated subpopulations were vulnerable to suppression; however, connected tributary subpopulations enhanced metapopulation persistence by serving as climate refuges. Water shortages driving changes in reservoir storage and subsequent warming would cause brown trout declines, but metapopulation QE was only achieved by re-focusing and increasing suppression. Our modeling approach improved our understanding of invasive brown trout metapopulation dynamics, which could lead to more focused and effective invasive species suppression strategies, and ultimately, maintenance of populations of endemic fishes

目次 : 『千葉医学雑誌 オープン・アクセス・ペーパー』 89E巻2号 2013年4月

Multivariate clustering. (DOCX 149 kb

Modeling spatial expansion of invasive alien species: relative contributions of environmental and anthropogenic factors to the spreading of the harlequin ladybird in France

Species distribution models (SDM) have often been used to predict the potential ranges of introduced species and prioritize management strategies. However, this approach assumes equilibrium between occurrences and environmental gradients, an assumption which is violated during the invasion process, where many suitable sites are empty because the species has not yet reached them. Here we considered the invasive ladybird Harmonia axyridis as a case study to show the benefits of using a dynamic colonization–extinction model that does not assume equilibrium. We used a multi-year occupancy model incorporating environmental, anthropogenic and neighborhood effects, to identify factors that explained spreading variation of this species in France from 2004, when only a few occupied sites were detected, to 2011. We found that anthropogenic factors (urbanization, agriculture, vineyards, and presence/absence of highways) explained more variation in the diffusion process than environmental factors (winter and summer temperatures, wind-speed, and rainfall). The surface of urbanization was the major anthropogenic factor increasing the probability of colonization. The average summer temperature was the main environmental factor affecting colonization, with a negative effect when high or low. The neighborhood effect revealed that colonization was mostly influenced by contributions coming from a radius of 24 km around the focal cell. The contribution of neighborhood decreases over time, suggesting that H. axyridis is reaching its equilibrium in France. This is confirmed by the small discrepancy observed between the performance of our approach and a SDM approach when predicting a single year occupancy pattern at the end of the study period. Our approach has the advantage of explicitly modelling the state of the biological system during the spatial expansion and identifying colonization constraints. This allows managers to explore the effect of different actions on the system at key moments of the invasion process, hence providing a powerful approach to prioritize management strategies

Biophysical and Socioeconomic Factors Associated with Forest Transitions at Multiple Spatial and Temporal Scales

Forest transitions (FT) occur when socioeconomic development leads to a shift from net deforestation to reforestation; these dynamics have been observed in multiple countries across the globe, including the island of Puerto Rico in the Caribbean. Starting in the 1950s, Puerto Rico transitioned from an agrarian to a manufacturing and service economy reliant on food imports, leading to extensive reforestation. In recent years, however, net reforestation has leveled off. Here we examine the drivers of forest transition in Puerto Rico from 1977 to 2000 at two subnational, nested spatial scales (municipality and barrio) and over two time periods (1977-1991 and 1991-2000). This study builds on previous work by considering the social and biophysical factors that influence both reforestation and deforestation at multiple spatial and temporal scales. By doing so within one analysis, this study offers a comprehensive understanding of the relative importance of various social and biophysical factors for forest transitions and the scales at which they are manifest. Biophysical factors considered in these analyses included slope, soil quality, and land-cover in the surrounding landscape. We also considered per capita income, population density, and the extent of protected areas as potential factors associated with forest change. Our results show that, in the 1977-1991 period, biophysical factors that exhibit variation at municipality scales (~100 km²) were more important predictors of forest change than socioeconomic factors. In this period, forest dynamics were driven primarily by abandonment of less productive, steep agricultural land in the western, central part of the island. These factors had less predictive power at the smaller barrio scale (~10 km²) relative to the larger municipality scale during this time period. The relative importance of socioeconomic variables for deforestation, however, increased over time as development pressures on available land increased. From 1991-2000, changes in forest cover reflected influences from multiple factors, including increasing population densities, land development pressure from suburbanization, and the presence of protected areas. In contrast to the 1977-1991 period, drivers of deforestation and reforestation over this second interval were similar for the two spatial scales of analyses. Generally, our results suggest that although broader socioeconomic changes in a given region may drive the demand for land, biophysical factors ultimately mediate where development occurs. Although economic development may initially result in reforestation due to rural to urban migration and the abandonment of agricultural lands, increased economic development may lead to deforestation through increased suburbanization pressures

Thinking like a consumer: Linking aquatic basal metabolism and consumer dynamics

The increasing availability of high-frequency freshwater ecosystem metabolism data provides an opportunity to identify links between metabolic regimes, as gross primary production and ecosystem respiration patterns, and consumer energetics with the potential to improve our current understanding of consumer dynamics (e.g., population dynamics, community structure, trophic interactions). We describe a conceptual framework linking metabolic regimes of flowing waters with consumer community dynamics. We use this framework to identify three emerging research needs: (1) quantifying the linkage of metabolism and consumer production data via food web theory and carbon use efficiencies, (2) evaluating the roles of metabolic dynamics and other environmental regimes (e.g., hydrology, light) in consumer dynamics, and (3) determining the degree to which metabolic regimes influence the evolution of consumer traits and phenology. Addressing these needs will improve the understanding of consumer biomass and production patterns as metabolic regimes can be viewed as an emergent property of food webs

The metabolic regimes of 356 rivers in the United States

A national-scale quantification of metabolic energy flow in streams and rivers can improve understanding of the temporal dynamics of in-stream activity, links between energy cycling and ecosystem services, and the effects of human activities on aquatic metabolism. The two dominant terms in aquatic metabolism, gross primary production (GPP) and aerobic respiration (ER), have recently become practical to estimate for many sites due to improved modeling approaches and the availability of requisite model inputs in public datasets. We assembled inputs from the U.S. Geological Survey and National Aeronautics and Space Administration for October 2007 to January 2017. We then ran models to estimate daily GPP, ER, and the gas exchange rate coefficient for 356 streams and rivers across the continental United States. We also gathered potential explanatory variables and spatial information for cross-referencing this dataset with other datasets of watershed characteristics. This dataset offers a first national assessment of many-day time series of metabolic rates for up to 9 years per site, with a total of 490,907 site-days of estimates.We thank Jill Baron and the USGS Powell Center for financial support for this collaborative effort (Powell Center Working Group title: "Continental-scale overview of stream primary productivity, its links to water quality, and consequences for aquatic carbon biogeochemistry"). Additional financial support came from the USGS NAWQA program and Office of Water Information. NSF grants DEB-1146283 and EF1442501 partially supported ROH. A post-doctoral grant from the Basque Government partially supported MA. NAG was supported by the U.S. Department of Energy's Office of Science, Biological and Environmental Research. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725. Leah Colasuonno provided expert logistical support of our working group meetings. The developers of USGS ScienceBase were very helpful both in hosting this dataset and in responding to our requests. Randy Hunt and Mike Fienen of the USGS Wisconsin Modeling Center graciously provided access to their HTCondor cluster. Mike Vlah provided detailed and insightful reviews of the data and metadata