Management Applications of Remotely Sensed Dynamic Seascapes: Two Case Studies

Dynamic seascapes, which are classified using a combination of remotely sensed data and model output, represent a potential tool for dynamic ecosystem-based management in marine systems. This work explores the utility of seascape classification in the context of marine resource management by examining two case studies: one involving biological relationships of species abundances and distributions to seascape classes in the California Current and another using seascapes to relate environmental variability to socioeconomic vulnerability in the Gulf of Alaska. In the first chapter, eight distinct seascape classes across a range of spatial and temporal scales are related to forage species assemblages on the continental shelf of California. Species and assemblage level associations with specific seascapes emerge. Despite a high number of ubiquitous species, seascapes are shown to have unique assemblages. Assemblage distinctness was strongest within the dominant seascape within 25km of trawl locations at a monthly temporal resolution. Occupancy likelihood within seascapes varied at the individual species level, resulting in positive and negative associations between key forage species like Northern Anchovy (Engraulis mordax), Pacific Sardine (Sardinops sagax), and juvenile rockfish (Sebastes spp.) and distinct seascape classes. The second chapter evaluates the ability of dynamic seascapes to represent the impact of multiple potential environmental stressors on the growth of early life stages of Pacific Cod (Gadus macrocephalus) and on the socioeconomic vulnerability of coupled human-natural systems in the Gulf of Alaska. Stress-scapes represent the dynamic footprint of Pacific Cod growth responses to changing partial pressure of carbon dioxide (pCO2) and sea-surface temperature (SST). Changing spatial extent of stress-scapes were associated with marine heatwave events and influenced the vulnerability of the social-ecological system of the GOA, resulting in changes to measured hazard and vulnerability for Alaskan communities at the Census Area level. Together these results show how dynamic seascapes can visualize and quantify the effects of environmental variability on living marine resources and how dynamic shifts in ocean habitat can translate to vulnerability across a diverse human socio-economic landscape. Future work can build on these methods and results to integrate dynamic seascape classification into existing and future ecosystem-based management and dynamic ocean management frameworks

Evaluating the Effects of Multiple Environmental Stressors on the Gulf of Alaska Pacific Cod Fishery Using a Dynamic Stress-Scape Framework

The Gulf of Alaska (GOA) is home to the most productive fisheries in the world. In 2019, 2.2 million metric tons of fish were shipped from Alaska to destinations all over the world (NOAA Fisheries, 2019). From 2014-2016 and, more recently, in 2019 the largest heatwaves in recorded history caused substantial reduction in Pacific Cod stocks. The most recent heatwave event resulted in the closure of the Pacific Cod fishery in 2020. These heatwave events have been the primary driver of changes to the GOA’s biogeochemistry; the effect of these changes on living marine resources are not well understood. Growing interest in these complex and changing ocean conditions motivates a need for biologically relevant models of the ocean. Deciphering the connection between optimum growth rate of juvenile Pacific Cod and environmental variables in GOA inspires this effort to visualize the multivariable clustering of the coastal GOA water in a way that is biologically relevant to the Pacific Cod Fishery. The proposed stress-scape visualization method utilizes the "Probabilistic Self-Organizing Map" (prSOM) algorithm, applying the algorithm to remotely sensed data and latent variables generated from bioenergetic models. The prSOM provides a topology-preserving classification of the nonlinear relationship between juvenile Pacific Cod growth rate and remotely sensed spatio-temporal environmental data. Visualization and analysis of the classification results given by the prSOM show that in heatwave years existing bioenergetics models for maturing juvenile Pacific Cod in coastal Gulf of Alaska nurseries experience adequate growth rates. This result coupled with the outcomes in the Pacific Cod Fishery in heatwave years highlights a need for a more thorough understanding of covariance between growth rate and environmental variables like CO₂ and pCO₂ and enables fishery scientists to infer that Pacific Cod are being affected at an earlier life stage. The visualization methods create a new direction for visualization that combines biological relevance and ocean data

Soft computing approaches to uncertainty propagation in environmental risk mangement

Real-world problems, especially those that involve natural systems, are complex and composed of many nondeterministic components having non-linear coupling. It turns out that in dealing with such systems, one has to face a high degree of uncertainty and tolerate imprecision. Classical system models based on numerical analysis, crisp logic or binary logic have characteristics of precision and categoricity and classified as hard computing approach. In contrast soft computing approaches like probabilistic reasoning, fuzzy logic, artificial neural nets etc have characteristics of approximation and dispositionality. Although in hard computing, imprecision and uncertainty are undesirable properties, in soft computing the tolerance for imprecision and uncertainty is exploited to achieve tractability, lower cost of computation, effective communication and high Machine Intelligence Quotient (MIQ). Proposed thesis has tried to explore use of different soft computing approaches to handle uncertainty in environmental risk management. The work has been divided into three parts consisting five papers. In the first part of this thesis different uncertainty propagation methods have been investigated. The first methodology is generalized fuzzy α-cut based on the concept of transformation method. A case study of uncertainty analysis of pollutant transport in the subsurface has been used to show the utility of this approach. This approach shows superiority over conventional methods of uncertainty modelling. A Second method is proposed to manage uncertainty and variability together in risk models. The new hybrid approach combining probabilistic and fuzzy set theory is called Fuzzy Latin Hypercube Sampling (FLHS). An important property of this method is its ability to separate randomness and imprecision to increase the quality of information. A fuzzified statistical summary of the model results gives indices of sensitivity and uncertainty that relate the effects of variability and uncertainty of input variables to model predictions. The feasibility of the method is validated to analyze total variance in the calculation of incremental lifetime risks due to polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/F) for the residents living in the surroundings of a municipal solid waste incinerator (MSWI) in Basque Country, Spain. The second part of this thesis deals with the use of artificial intelligence technique for generating environmental indices. The first paper focused on the development of a Hazzard Index (HI) using persistence, bioaccumulation and toxicity properties of a large number of organic and inorganic pollutants. For deriving this index, Self-Organizing Maps (SOM) has been used which provided a hazard ranking for each compound. Subsequently, an Integral Risk Index was developed taking into account the HI and the concentrations of all pollutants in soil samples collected in the target area. Finally, a risk map was elaborated by representing the spatial distribution of the Integral Risk Index with a Geographic Information System (GIS). The second paper is an improvement of the first work. New approach called Neuro-Probabilistic HI was developed by combining SOM and Monte-Carlo analysis. It considers uncertainty associated with contaminants characteristic values. This new index seems to be an adequate tool to be taken into account in risk assessment processes. In both study, the methods have been validated through its implementation in the industrial chemical / petrochemical area of Tarragona. The third part of this thesis deals with decision-making framework for environmental risk management. In this study, an integrated fuzzy relation analysis (IFRA) model is proposed for risk assessment involving multiple criteria. The fuzzy risk-analysis model is proposed to comprehensively evaluate all risks associated with contaminated systems resulting from more than one toxic chemical. The model is an integrated view on uncertainty techniques based on multi-valued mappings, fuzzy relations and fuzzy analytical hierarchical process. Integration of system simulation and risk analysis using fuzzy approach allowed to incorporate system modelling uncertainty and subjective risk criteria. In this study, it has been shown that a broad integration of fuzzy system simulation and fuzzy risk analysis is possible. In conclusion, this study has broadly demonstrated the usefulness of soft computing approaches in environmental risk analysis. The proposed methods could significantly advance practice of risk analysis by effectively addressing critical issues of uncertainty propagation problem.Los problemas del mundo real, especialmente aquellos que implican sistemas naturales, son complejos y se componen de muchos componentes indeterminados, que muestran en muchos casos una relación no lineal. Los modelos convencionales basados en técnicas analíticas que se utilizan actualmente para conocer y predecir el comportamiento de dichos sistemas pueden ser muy complicados e inflexibles cuando se quiere hacer frente a la imprecisión y la complejidad del sistema en un mundo real. El tratamiento de dichos sistemas, supone el enfrentarse a un elevado nivel de incertidumbre así como considerar la imprecisión. Los modelos clásicos basados en análisis numéricos, lógica de valores exactos o binarios, se caracterizan por su precisión y categorización y son clasificados como una aproximación al hard computing. Por el contrario, el soft computing tal como la lógica de razonamiento probabilístico, las redes neuronales artificiales, etc., tienen la característica de aproximación y disponibilidad. Aunque en la hard computing, la imprecisión y la incertidumbre son propiedades no deseadas, en el soft computing la tolerancia en la imprecisión y la incerteza se aprovechan para alcanzar tratabilidad, bajos costes de computación, una comunicación efectiva y un elevado Machine Intelligence Quotient (MIQ). La tesis propuesta intenta explorar el uso de las diferentes aproximaciones en la informática blanda para manipular la incertidumbre en la gestión del riesgo medioambiental. El trabajo se ha dividido en tres secciones que forman parte de cinco artículos. En la primera parte de esta tesis, se han investigado diferentes métodos de propagación de la incertidumbre. El primer método es el generalizado fuzzy α-cut, el cual está basada en el método de transformación. Para demostrar la utilidad de esta aproximación, se ha utilizado un caso de estudio de análisis de incertidumbre en el transporte de la contaminación en suelo. Esta aproximación muestra una superioridad frente a los métodos convencionales de modelación de la incertidumbre. La segunda metodología propuesta trabaja conjuntamente la variabilidad y la incertidumbre en los modelos de evaluación de riesgo. Para ello, se ha elaborado una nueva aproximación híbrida denominada Fuzzy Latin Hypercube Sampling (FLHS), que combina los conjuntos de la teoría de probabilidad con la teoría de los conjuntos difusos. Una propiedad importante de esta teoría es su capacidad para separarse los aleatoriedad y imprecisión, lo que supone la obtención de una mayor calidad de la información. El resumen estadístico fuzzificado de los resultados del modelo generan índices de sensitividad e incertidumbre que relacionan los efectos de la variabilidad e incertidumbre de los parámetros de modelo con las predicciones de los modelos. La viabilidad del método se llevó a cabo mediante la aplicación de un caso a estudio donde se analizó la varianza total en la cálculo del incremento del riesgo sobre el tiempo de vida de los habitantes que habitan en los alrededores de una incineradora de residuos sólidos urbanos en Tarragona, España, debido a las emisiones de dioxinas y furanos (PCDD/Fs). La segunda parte de la tesis consistió en la utilización de las técnicas de la inteligencia artificial para la generación de índices medioambientales. En el primer artículo se desarrolló un Índice de Peligrosidad a partir de los valores de persistencia, bioacumulación y toxicidad de un elevado número de contaminantes orgánicos e inorgánicos. Para su elaboración, se utilizaron los Mapas de Auto-Organizativos (SOM), que proporcionaron un ranking de peligrosidad para cada compuesto. A continuación, se elaboró un Índice de Riesgo Integral teniendo en cuenta el Índice de peligrosidad y las concentraciones de cada uno de los contaminantes en las muestras de suelo recogidas en la zona de estudio. Finalmente, se elaboró un mapa de la distribución espacial del Índice de Riesgo Integral mediante la representación en un Sistema de Información Geográfico (SIG). El segundo artículo es un mejoramiento del primer trabajo. En este estudio, se creó un método híbrido de los Mapas Auto-organizativos con los métodos probabilísticos, obteniéndose de esta forma un Índice de Riesgo Integrado. Mediante la combinación de SOM y el análisis de Monte-Carlo se desarrolló una nueva aproximación llamada Índice de Peligrosidad Neuro-Probabilística. Este nuevo índice es una herramienta adecuada para ser utilizada en los procesos de análisis. En ambos artículos, la viabilidad de los métodos han sido validados a través de su aplicación en el área de la industria química y petroquímica de Tarragona (Cataluña, España). El tercer apartado de esta tesis está enfocado en la elaboración de una estructura metodológica de un sistema de ayuda en la toma de decisiones para la gestión del riesgo medioambiental. En este estudio, se presenta un modelo integrado de análisis de fuzzy (IFRA) para la evaluación del riesgo cuyo resultado depende de múltiples criterios. El modelo es una visión integrada de las técnicas de incertidumbre basadas en diseños de valoraciones múltiples, relaciones fuzzy y procesos analíticos jerárquicos inciertos. La integración de la simulación del sistema y el análisis del riesgo utilizando aproximaciones inciertas permitieron incorporar la incertidumbre procedente del modelo junto con la incertidumbre procedente de la subjetividad de los criterios. En este estudio, se ha demostrado que es posible crear una amplia integración entre la simulación de un sistema incierto y de un análisis de riesgo incierto. En conclusión, este trabajo demuestra ampliamente la utilidad de aproximación Soft Computing en el análisis de riesgos ambientales. Los métodos propuestos podría avanzar significativamente la práctica de análisis de riesgos de abordar eficazmente el problema de propagación de incertidumbre

Dynamic seascapes : a quantitative framework for scaling pelagic ecology and biogeochemistry

Understanding and modeling microbial responses and feedbacks to climate change is hampered by a lack of a framework in the pelagic environment by which to link local mechanism to large scale patterns. Where terrestrial ecology draws from landscape theory and practice to address issues of scale, the pelagic seascape concept is still in its infancy. We have applied the patch mosaic paradigm of landscape ecology to the study of the seasonal and interannual variability of the North Pacific to facilitate comparative analysis between pelagic ecosystems and provide spatiotemporal context for eulerian time-series studies. Using multivariate, 13-year climatologies of sea surface temperature, photosynthetically active radiation, and chlorophyll a derived from remote sensing observations, we classified hierarchical seascapes at monthly and interannual scales. These dynamic, objectively-determined seascapes offer improved hydrographic coherence relative to oceanic regions with subjectively defined and static boundaries (Chapter 2) and represent unique biogeochemical functioning (Chapter 2) and microbial communities (Chapter3). Furthermore they provide consilience between satellite studies and in situ observations (Chapter 4) and allow for objective comparison of ecosystem forcing (Chapters, 4 and 5). In Chapter 2, we rigorously tested the assumption that satellite-derived seascapes describe regions of biogeochemical coherence. The seasonal cycle of the North Pacific was characterized at three levels of spatiotemporal hierarchy and broader relevance of monthly –resolved seascapes was assessed through analysis of variance (ANOVA) and multiple linear regression (MLR) analyses of nutrient, primary productivity, and pCO₂ data. Distinct nutrient and primary productivity regimes were well-characterized in the coarsest two levels of hierarchy (ANOVA, R² = 0.5-0.7). Finer scale partitioning was more relevant for pCO₂. MLR analyses revealed differential forcing on pCO₂ across seascapes and hierarchical levels and a 33 % reduction in mean model error with increased partitioning (from 18.5 µatm to 12.0 µatm pCO₂). In Chapter 3 we verified the seascapes with in situ collections of microbial abundance and structure. Flow cytometry data was collected from two long term time series and several cruises spanning thousand kilometers of the NE Pacific; these data allowed us to quantify spatiotemporal patterns. In addition, multiple response permutation analysis revealed differences in community structure across discrete seascapes, in terms of both absolute and relative abundances. Principal component analysis of the assemblage supported seascape divisions and revealed structure along environmental gradients with strong associations with chlorophyll a and sea surface temperature and, to a lesser extent, with mixed layer depth and mean photosynthetically active radiation in the mixed layer. Differences of assemblage structure between seascapes and strength of environmental forcing were strong in the subarctic and transition zones, but less pronounced in the subtropics, suggesting satellite-detected changes in bulk properties that may be associated with local physiology or interannual shifts in seascape boundaries. Based on the work presented in Chapter 4, we discovered that interannual shifts in the boundaries of a transition seascape and two distinct oligotrophic subtropical seascapes affect the variability observed at benchmark time series Station ALOHA; the latter two seascapes oscillate in their contributions to the expansion of the entire subtropics. On interannual scales, in situ phytoplankton abundance (as measured by chl-a), net primary productivity (NPP), and the relative abundance of eukaryotic phytoplankton and Synechococcus sp. increased during periods of encroachment by the transition seascape. Conversely, the relative abundance of Prochlorococcus increased and chl –a and NPP decreased when the highly oligotrophic seascape encroached on Station ALOHA. The dynamic range (~6 million km²) of subtropical expansion is born almost entirely by the transition zone - resulting in a transfer of ~1.2 Pg of total primary C production between a system primed for export production and one dominated by the microbial loop. In Chapter 5, we investigated multiple factors that contribute to the effectiveness of the biological pump in the transition seascape. Near-continuous measurements of net primary production (NPP), net community production (NCP) and several ecophysiological variables were collected in across subarctic, transition, and subtropical seascapes of the Northeast Pacific during August and September of 2008. Mesoscale processes and shifts in community structure contributed to high export efficiency in the subtropical seascape; the convergence of assemblage structure, high biomass, moderate NPP: NCP and high NCP contributed to biologically mediated air-sea exchange in the transition seascape. Furthermore, NPP and NCP were strongly spatially coupled in both the transition (r[subscript 1, 39]=0.70; p<0.0001) and subtropical seascapes (r[subscript 1, 45]= 0.68, p<0.0001), suggesting the possibility for empirical modeling efforts. This dissertation provides a first step to characterize the seascape variability in the NE Pacific and to understand the modulation of primary and export production in a critical transition region. The multivariate seascape approach described here provides spatiotemporal context for in situ studies and allows objective comparisons of systems' responses to climatic forcing. An integrated ocean observing system will require insight from in situ observations and experiments, ecosystem models, and satellite remote sensing. The results highlighted in this dissertation suggest that the pelagic seascape framework, through its capacity to scale both context and mechanism, may serve as an important and unifying component of such an observing system