Humanity's interactions with the supporting environment are, to state the obvious, complex. Humanity's industrial activities effect the environment over time and space, and the same activities even produce different results in different locations. Since the complexities of these interactions may preclude the successful use of eco-performance metrics, humanity may need a means of informing environmental management decisions that accounts for changes with time, spatial patterns and local uniqueness. The objective of this effort is to interface engineering and ecological systems models to better estimate environmental impacts by modeling the dynamic, spatially explicit and location dependent changes caused by industrial activities. Building upon previously developed, dynamic, spatially explicit, location specific ecosystem modeling software, a technical framework for estimating the impacts of industrial systems in ecosystems is developed. Ecological disturbances endemic to engineering systems are integrated into these existing ecosystem models. The results of these integrations are discussed, and from these results, the potential for estimating impacts using dynamic, spatially explicit and location based modeling is evaluated. In other words, one learns the result of placing industrial plants in mother natures garden.

Fisheries managers have the potential to significantly improve reef fish management in the Gulf of Mexico through the use of ecosystem-based approaches to fisheries management. Ecosystem-based approaches are needed to address the effects of fishing on trophodynamic interactions, to better account for ecosystem-scale processes in model projections, and to recognize the short and long-term biomass tradeoffs associated with making regulatory choices. My research was concentrated around three objectives: (1) characterizing the trophodynamic interactions between Gulf of Mexico fishes, in order to construct an invaluable tool (a Gulf of Mexico Atlantis model) to be used in ecological hypothesis testing and policy performance evaluation for years to come; (2) predicting ecological indicators for the Gulf of Mexico that both respond to fishing pressure and are robust to observational error, and; (3) evaluating the performance of an ecosystem-based policy options for managing reef fish species in the Gulf of Mexico. To accomplish these objectives, a spatial, trophodynamic ecosystem model- Atlantis, was employed to represent the Gulf of Mexico marine ecosystem. To characterize trophic interactions between modeled species, I applied a maximum likelihood estimation procedure to produce Dirichlet probability distributions representing the likely contribution of prey species to predators’ diets. This provided mode values (the peak of the distribution) and associated error ranges, which describe the likely contribution of a prey item in a predator’s diet. The mode values were used to parameterize the availabilities (diet) matrix of the Gulf of Mexico Atlantis model. Investigating trophic interactions was useful for determining which species within the Atlantis model were data rich, and justified the emphasis on reef fish species and their prey items in subsequent analyses. Once parameterized and calibrated, I used the Atlantis model to project ecological indicators over a 50 year time horizon (2010-2060) under varying levels of fishing mortality. Principal component analysis was used to evaluate ecological indicator trajectories in multivariate space, to rank indicators according to how well they describe variability in ecosystem structure (termed ‘importance’), to reveal redundancies in the information conveyed, to quantify interannual noise and to determine how robust indicators are to observational error. Reef fish catch, Red snapper biomass, King mackerel biomass and Species richness indicators ranked the highest in terms of importance and robustness to error and in having low levels of interannual noise (i.e., requiring less frequent monitoring). I then used a management strategy evaluation (MSE) framework in Atlantis to evaluate some of these same indicators under an ecosystem-based approach to fisheries management – using robust harvest control rules to manage reef fishes. I found that this ecosystem-based policy option was able to maintain higher reef fish biomass, catch and ecosystem-wide biodiversity under any given level of fishing mortality when compared to a status quo management approach. These results suggest that harvesting under the HCRs encourages an alternative ecosystem state with a more Pareto-efficient tradeoff frontier than the status-quo policy. A potentially reduced extinction risk for reef fish is plausible under this ecosystem-based policy option. This research provides a quantitative look at the fishery performance and ecological tradeoffs associated with various policy options. MSE methodology using ecosystem-based policy performance metrics is also demonstrated. Tool development and findings from this research should aid in the development of ecosystem-based policies for this region.

Le réservoir de carbone de sol est un élément clé du cycle global du carbone et donc du système climatique. Les sols et le carbone organique qu'ils contiennent constituent le plus grand réservoir de carbone des écosystèmes terrestres. Ce réservoir est également responsable du stockage d'une grande quantité de carbone prélevé de l'atmosphère par les plantes par la photosynthèse. C'est pourquoi les sols sont considérés comme une stratégie de mitigation viable pour réduire la concentration atmosphérique de CO2 dûe aux émissions globales de CO2 d'origine fossile. Malgré son importance, des incertitudes subsistent quant à la taille du réservoir global de carbone organique de sol et à ses dynamiques. Les modèles de biosphère terrestre sont des outils essentiels pour quantifier et étudier la dynamique du carbone organique de sol. Ces modèles simulent les processus biophysiques et biogéochimiques au sein des écosystèmes et peuvent également simuler le comportement futur du réservoir de carbone organique de sol en utilisant des forçages météorologiques appropriés. Cependant, de grandes incertitudes dans les projections faite par les modèles de biosphère terrestre sur les dynamiques du carbone organique de sol ont été observées, en partie dues au problème de l'équifinalité. Afin d'améliorer notre compréhension de la dynamique du carbone organique de sol, cette recherche visait à optimiser les paramètres du schéma de carbone de sol contenu dans le modèle de schéma canadien de surface terrestre incluant les cycles biogéochimiques (CLASSIC), afin de parvenir à une meilleure représentation de la dynamique du carbone organique de sol. Une analyse de sensibilité globale a été réalisée pour identifier lesquels parmis les 16 paramètres du schéma de carbone de sol, n'affectaient pas la simulation du carbone organique de sol et de la respiration du sol. L'analyse de sensibilité a utilisé trois sites de covariance des turbulences afin de représenter différentes conditions climatiques simulées par le schéma de carbone de sol et d'économiser le coût calculatoire de l'analyse. L'analyse de sensibilité a démontré que certains paramètres du schéma de carbone de sol ne contribuent pas à la variance des simulations du carbone organique de sol et de la respiration du sol. Ce résultat a permis de réduire la dimensionnalité du problème d'optimisation. Ensuite, quatre scénarios d'optimisation ont été élaborés sur la base de l'analyse de sensibilité, chacun utilisant un ensemble de paramètres. Deux fonctions coûts ont été utilisées pour l'optimisation de chacun des scénarios. L'optimisation a également démontré que la fonction coût utilisée avait un impact sur les ensembles de paramètres optimisés. Les ensembles de paramètres obtenus à partir des différents scénarios et fonctions coûts ont été comparés à des ensembles de données indépendants et à des estimations globales du carbone organique de sol à l'aide de métrique tel la racine de l'erreur quadratique moyenne et le bias, afin d'évaluer l'effet des ensembles de paramètres sur les simulations effectuées par le schéma de carbone de sol. Un ensemble de paramètres a surpassé les autres ensembles de paramètres optimisés ainsi que le paramétrage par défaut du modèle. Ce résultat a indiqué que la structure d'optimisation était en mesure de produire un ensemble de paramètres qui simulait des valeurs de carbone organique de sol et de respiration du sol qui étaient plus près des valeurs observées que le modèle CLASSIC par défaut, améliorant la représentation de la dynamique du carbone du sol. Cet ensemble de paramètres optimisés a ensuite été utilisé pour effectuer des simulations futures (2015-2100) de la dynamique du carbone organique de sol afin d'évaluer son impact sur les projections de CLASSIC. Les simulations futures ont montré que l'ensemble de paramètres optimisés simulait une quantité de carbone organique de sol 62 % plus élevée que l'ensemble de paramètres par défaut tout en simulant des flux de respiration du sol similaires. Les simulations futures ont également montré que les ensembles de paramètres optimisés et par défaut prévoyaient que le réservoir de carbone organique de sol demeurerait un puits de carbone net d'ici 2100 avec des sources nettes régionales. Cette étude a amélioré globalement la représentation de la dynamique du carbone organique de sol dans le schéma de carbone de sol de CLASSIC en fournissant un ensemble de paramètres optimisés. Cet ensemble de paramètres devrait permettre d'améliorer notre compréhension de la dynamique du carbone du sol. / The soil carbon pool is a vital component of the global carbon cycle and, therefore, the climate system. Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems. This pool stores a large quantity of carbon that plants have removed from the atmosphere through photosynthesis. Because of this, soils are considered a viable climate change mitigation strategy to lower the global atmospheric CO2 concentration that is presently being driven higher by anthropogenic fossil CO2 emissions. Despite its importance, there are still considerable uncertainties around the size of the global SOC pool and its response to changing climate. Terrestrial biosphere models (TBM) simulate the biogeochemical processes within ecosystems and are critical tools to quantify and study SOC dynamics. These models can also simulate the future behavior of SOC if carefully applied and given the proper meteorological forcings. However, TBM predictions of SOC dynamics have high uncertainties due in part to equifinality. To improve our understanding of SOC dynamics, this research optimized the parameters of the soil carbon scheme contained within the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC), to better represent SOC dynamics. A global sensitivity analysis was performed to identify which of the 16 parameters of the soil carbon scheme did not affect simulated SOC stocks and soil respiration (Rsoil). The sensitivity analysis used observations from three eddy covariance sites for computational efficiency and to encapsulate the climate represented by the global soil carbon scheme. The sensitivity analysis revealed that some parameters of the soil carbon scheme did not contribute to the variance of simulated SOC and Rsoil. These parameters were excluded from the optimization which helped reduce the dimensionality of the optimization problem. Then, four optimization scenarios were created based on the sensitivity analysis, each using a different set of parameters to assess the impact the number of parameters included had on the optimization. Two different loss functions were used in the optimization to assess the impact of accounting for observational error. Comparing the optimal parameters between the optimizations performed using the different loss functions showed that the loss functions impacted the optimized parameter sets. To determine which optimized parameter set obtained by each loss function was most skillful, they were compared to independent data sets and global estimates of SOC, which were not used in the optimization using comparison metrics based on root-mean-square-deviation and bias. This study generated an optimal parameter set that outperformed the default parameterization of the model. This optimal parameter set was then applied in future simulations of SOC dynamics to assess its impact upon CLASSIC's future projections. These future simulations showed that the optimal parameter set simulated future global SOC content 62 % higher than the default parameter set while simulating similar Rsoil fluxes. The future simulations also showed that both the optimized and default parameter sets projected that the SOC pool would be a net sink by 2100 with regional net sources, notably tropical regions.

As political debate over the overexploitation of fish stocks rages on, UBC’s Fisheries Centre is targeting the responsible management of aquatic ecosystems from multiple perspectives.

<p dir="ltr">Global forest ecosystems provide essential ecosystem services that contribute to water and climate regulation, food production, recreation, and raw materials. They also serve as crucial habitats for numerous terrestrial species of amphibians, birds, and mammals worldwide. However, recent decades have witnessed unprecedented changes in forest ecosystems due to climate change, shifts in species distribution patterns, increased planted forest areas, and various disturbances such as forest fires, insect infestations, and urbanization. These changes can have far-reaching impacts on ecological networks, human well-being, and the well-being of global forest ecosystems. To address these challenges, I present four studies to quantify forest dynamics through mapping, classification, and projection, using artificial intelligence tools in combination with a vast amount of training data. (I) I present a spatially continuous map of planted forest distribution across East Asia, produced by integrating multiple sources of planted and natural forest data. I found that China contributed 87% of the total planted forest areas in East Asia, most of which are located in the lowland tropical/subtropical regions and Sichuan Basin. I also estimated the dominant genus in each planted forest location. (II) I used continent-wide forest inventory data to compare the range shifts of forest types and their constituent tree species in North America in the past 50 years. I found that forest types shifted more than three times as fast as the average of their constituent tree species. This marked difference was attributable to a predominant positive covariance between tree species ranges and the change of species relative abundance. (III) Based on individual-level field surveys of trees and breeding birds across North America, I characterized New World wood-warbler (<i>Parulidae</i>) species richness and its potential drivers. I identified forest type as the most powerful predictor of New World wood-warbler species richness, which adds valuable evidence to the ongoing physiognomy versus composition debate among ornithologists. (IV) In the appendix, I utilized continent-wide forest inventory data from North America and South America and the combination of supervised and unsupervised machine learning algorithms to produce the first data-driven map of forest types in the Americas. I revealed the distribution of forest types, which are useful for cost-effective forest and biodiversity management and planning. Taken together, these studies provide insight into the dynamics of forest ecosystems at a large geographic scale and have implications for effective decision-making in conservation, management, and global restoration programs in the midst of ongoing global change.</p>