Hermatypic, or reef-building, corals (Order Scleractinia) are the foundation of coral reefs, providing a diversity of structurally complex habitats for the myriad species in these biologically diverse ecosystems. However, both local and global anthropogenic stressors threaten the persistence of these corals. For this thesis, thirty 16m2 permanent photoquadrats at 10 shallow forereef sites around Kiritimati (Christmas Island, Republic of Kiribati) were monitored across a four-year study encompassing the 2015-2016 El Niño derived marine heatwave, and subsequent mass coral mortality event. Sites were exposed to either low, medium, or high levels of local anthropogenic disturbance. My objective herein was to examine the effects of a mass coral mortality event on reef structural complexity, from the end of the event to three years afterwards. To do so, I digitally quantified six metrics of structural complexity for each photoquadrat sampled across three resolution scales for each of the five expeditions. Plots from 2015, 2017, and 2019 were later annotated based on the morphological structure present. I found that while significant declines in multiple of habitat metrics occurred by the end of the heatwave, no further significant declines occurred thereafter. However, this trend was lost as resolution scale increased, indicating the trends seen in the habitat metrics at 1.0 cm were likely documenting the shift from live coral towards abiotic dominated reefs. Anthropogenic disturbance compounded the El Niño’s effect, ensuring high disturbance sites had the lowest structural complexity values throughout the study. Lastly, live branching, tabulate, foliose, and submassive coral morphologies were found to be most closely associated with the different habitat complexity metrics. These results highlight the importance live coral structure has on reef structural complexity, illustrate the importance of model resolution when quantifying trends in structural complexity, pinpoint coral morphologies creating reef structural complexity, and further emphasize the need to limit the effects of local anthropogenic disturbance on coral reefs. / Graduate / 2023-04-15

No description available.

To achieve a high level of performance and efficiency, contemporary aerospace systems must become increasingly complex. While complexity management traditionally focuses on a product’s components and their interconnectedness, organizational representation in complexity analysis is just as essential. This thesis addresses this organizational aspect of complexity through an Organizational Complexity Metric (OCM) to aid complexity management. The OCM augments Sinha’s structural complexity metric for product architectures into a metric that can be applied to organizations. Utilizing nested numerical design structure matrices (DSMs), a compact visual representation of organizational complexity was developed. Within the nested numerical DSM are existing organizational datasets used to quantify the complexity of both organizational system components and their interfaces. The OCM was applied to a hypothetical system example, as well as an existing aerospace organizational architecture. Through the development of the OCM, this thesis assumed that each dataset was collected in a statistically sufficient manner and has a reasonable correlation to system complexity. This thesis recognizes the lack of complete human representation and aims to provide a platform for expansion. Before a true organizational complexity metric can be applied to real systems, additional human considerations should be considered. These limitations differ from organization to organization and should be taken into consideration before implementation into a working system. The visualization of organizational complexity uses a color gradient to show the relative complexity density of different parts of the organization.

In this thesis I present and test a methodology for developing a stand scale index of structural complexity. If properly designed such an index can act as a summary variable for a larger set of stand structural attributes, providing a means of ranking stands in terms of their structural complexity, and by association, their biodiversity and vegetation condition. This type of index can also facilitate the use of alternative policy instruments for biodiversity conservation, such as mitigation banking, auctions and offsets, that rely on a common currency – the index value – that can be compared or traded between sites. My intention was to establish a clear and documentable methodology for developing a stand scale index of structural complexity, and to test this methodology using data from real stands.¶ As a starting point, I reviewed the literature concerning forest and woodland structure and found there was no clear definition of stand structural complexity, or definitive suite of structural attributes for characterising it. To address this issue, I defined stand structural complexity as a combined measure of the number of different structural attributes present in a stand, and the relative abundance of each of these attributes. This was analogous to approaches that have quantified diversity in terms of the abundance and richness of elements. It was also concluded from the review, that stand structural complexity should be viewed as a relative, rather than absolute concept, because the potential levels of different structural attributes are bound within certain limits determined by the inherent characteristics of the site in question, and the biota of the particular community will have evolved to reflect this range of variation. This implied that vegetation communities with naturally simple structures should have the potential to achieve high scores on an index of structural complexity.¶ I proposed the following five-stage methodology for developing an index of stand structural complexity: 1. Establish a comprehensive suite of stand structural attributes as a starting point for developing the index, by reviewing studies in which there is an established relationship between elements of biodiversity and structural attributes. 2. Develop a measurement system for quantifying the different attributes included in the comprehensive suite. 3. Use this measurement system to collect data from a representative set of stands across the range of vegetation condition (highly modified to unmodified) and developmental stages (regrowth to oldgrowth) occurring in the vegetation communities in which the index is intended to operate. 4. Identify a core set of structural attributes from an analysis of these data. 5. Combine the core attributes in a simple additive index, in which attributes are scored relative to their observed levels in each vegetation community.¶ Stage one of this methodology was addressed by reviewing a representative sample of the literature concerning fauna habitat relationships in temperate Australian forests and woodlands. This review identified fifty-five studies in south-east and south-west Australia, in which the presence or abundance of different fauna were significantly (p&lt0.05) associated with vegetation structural attributes. The majority of these studies concerned bird, arboreal mammal, and ground mammal habitat requirements, with relatively fewer studies addressing the habitat requirements of reptiles, invertebrates, bats or amphibians. Thirty four key structural attributes were identified from these fifty-five studies, by grouping similar attributes, and then representing each group with a single generic attribute. This set, in combination with structural attributes identified in the earlier review, provided the basis for developing an operational set of stand level attributes for the collection of data from study sites.¶ To address stages two and three of the methodology, data were collected from one woodland community –Yellow Box-Red Gum (E. melliodora-E. Blakelyi ) – and two dry sclerophyll forest communities – Broadleaved Peppermint-Brittle Gum (E. dives-E. mannifera ), Scribbly Gum-Red Stringybark (E. rossii E. macrorhyncha ) – in a 15,000 km2 study area in the South eastern Highlands Bioregion of Australia. A representative set of 48 sites was established within this study area, by identifying 24 strata, on the basis of the three vegetation communities, two catchments, two levels of rainfall and two levels of condition, and then locating two sites (replicates) within each stratum. At each site, three plots were systematically established, to provide an unbiased estimate of stand level means for 75 different structural attributes.¶ I applied a three-stage analysis to identify a core set of attributes from these data. The first stage – a preliminary analysis – indicated that the 48 study sites represented a broad range of condition, and that the two dry sclerophyll communities could be treated as a single community, which was structurally distinct from the woodland community. In the second stage of the analysis, thirteen core attributes were dentified using the criteria that a core attribute should:¶ 1. Be either, evenly or approximately normally distributed amongst study sites; 2. Distinguish between woodland and dry sclerophyll communities; 3. Function as a surrogate for other attributes; 4. Be efficient to measure in the field. The core attributes were: Vegetation cover &lt0.5m Vegetation cover 0.5-6.0m; Perennial species richness; Lifeform richness; Stand basal area of live trees; Quadratic mean diameter of live stems; ln(number of regenerating stems per ha+1); ln(number of hollow bearing trees per ha+1);ln(number of dead trees per ha+1);sqrt(number of live stems per ha &gt40cm dbh); sqrt(total log length per ha); sqrt(total largelog length per ha); Litter dry weight per ha. This analysis also demonstrated that the thirteen core attributes could be modelled as continuous variables, and that these variables were indicative of the scale at which the different attributes operated.¶ In the third and final stage of the analysis, Principal Components Analysis was used to test for redundancy amongst the core attributes. Although this analysis highlighted six groupings, within which attributes were correlated to some degree, these relationships were not considered sufficiently robust to justify reducing the number of core attributes.¶ The thirteen core attributes were combined in a simple additive index, in which, each attribute accounted for 10 points in a total index value of 130. Attributes were rescaled as a score from 0-10, using equations that modelled attribute score as a function of the raw attribute data. This maintained a high correlation (r > 0.97, p< 0.0001) between attribute scores and the original attribute data. Sensitivity analysis indicated that the index was not sensitive to attribute weightings, and on this basis attributes carried equal weight. In this form my index was straightforward to apply, and approximately normally distributed amongst study sites.¶ I demonstrated the practical application of the index in a user-friendly spreadsheet, designed to allow landowners and managers to assess the condition of their vegetation, and to identify management options. This spreadsheet calculated an index score from field data, and then used this score to rank the site relative to a set of reference sites. This added a regional context to the operation of the index, and is a potentially useful tool for identifying sites of high conservation value, or for identifying sites where management actions have maintained vegetation quality. The spreadsheet also incorporated the option of calculating an index score using a subset of attributes, and provided a measure of the uncertainty associated with this score.¶ I compared the proposed index with five prominent indices used to quantify vegetation condition or habitat value in temperate Australian ecosystems. These were: Newsome and Catling’s (1979) Habitat Complexity Score, Watson et al.’s (2001) Habitat Complexity Score, the Site Condition Score component of the Habitat Hectares Index of Parkes et al. (2003), the Vegetation Condition Score component of the Biodiversity Benefits Index of Oliver and Parkes (2003), and the Vegetation Condition Score component of the BioMetric Assessment Tool of Gibbons et al. (2004). I found that my index differentiated between study sites better than each of these indices. However, resource and time constraints precluded the use of a new and independent data set for this testing, so that the superior performance of my index must be interpreted cautiously.¶ As a group, the five indices I tested contained attributes describing compositional diversity, coarse woody debris, regeneration, large trees and hollow trees – these were attributes that I also identified as core ones. However, unlike these indices, I quantified weeds indirectly through their effect on indigenous plant diversity, I included the contribution of non-indigenous species to vegetation cover and did not apply a discount to this contribution, I limited the direct assessment of regeneration to long-lived overstorey species, I used stand basal area as a surrogate for canopy cover, I quantified litter in terms of biomass (dry weight) rather than cover, and I included the additional attributes of quadratic mean diameter and the number of dead trees.¶ I also concluded that Parkes et al. (2003), Oliver and Parkes (2003), and Gibbons et al. (2004), misapplied the concept of benchmarking, by characterising attributes in terms of a benchmark range or average level. This ignored processes that underpin variation at the stand level, such as the increased development of some attributes at particular successional stages, and the fact that attributes can respond differently to disturbance agents. It also produced indices that were not particularly sensitive to the differences in attribute levels occurring between stands. I suggested that a more appropriate application of benchmarking would be at the overarching level of stand structural complexity, using a metric such as the index developed in this thesis. These benchmarks could reflect observed levels of structural complexity in unmodified natural stands at different successional stages, or thresholds for structural complexity at which a wide range of biota are present, and would define useful goals for guiding on-ground management.

<p>In the last three decades, complexity theory has gained a lot of importance in several scientific disciplines like astronomy, geology, chemistry etc. It has slowly extended its usage in the field of project management. While trying to understand the managerial demands of modern day projects and the different situations faced in projects, the term ‘complexity’ is progressively becoming a benchmark term. In the recent past some of the challenging projects that have been completed are the Heathrow Terminal 5 and the construction of venues for the Beijing Olympics. But can we call these projects complex?It is probably too simplistic to classify projects as complex or non-complex. What is particularly important is to identify the source of the complexity, the level and also the implications of the complexity. Several academicians have studied the different dimensions and established different classifications of complexity. These are put together into models of complexity.But is this classification well-grounded in reality? This is what we aim to explore through this research. The specific questions that we wish to explore by conducting this research are:</p><ul><li>How does the understanding of project complexity in actuality conform to the theoretical complexity models?</li></ul><p>In an effort to answer the primary question, our study will also throw some light on factors of complexity across different sectors. We hope that this distinction will pave way for further research within these sectors. This now brings us to our sub-question:- How do the factors that contribute to complexity compare across different sectors?At the outset of this research, the literature on complexity was reviewed. An attempt was made to understand what complexity means with a focus on the field of project management.It was observed that there is a new wave of thinking in this field and a camp which believes that regular project management tools and techniques cannot be used for complex projects.</p><p>This has drawn several academicians to generate models of complexity based on various factors. In this research we have focused on some important models like that of Turner and Cochrane, Ralph Stacey, Terry Williams, Kahane and Remington and Pollack. We have tried to see if any of these models fit in with how practitioners understand complexity.To find out how practitioners comprehend complexity, we followed a grounded theory approach and also used quantitative methods to supplement the results in accordance in a mixed methodology. Semi-structured interviews were carried out with nine project managers from different sectors and different  geographical locations. The interviews were analyzed and the data was broken down to different categories referred to as open coding where labelling was done. This was followed by Axial coding where we describe the properties and build relations between these categories. The final stage is selective coding where the emerged theory is integrated and refined.Quantitative data was collected through a short questionnaire which listed out some factors which could cause or lead to complexity in projects. A total of 29 responses were obtained for the questionnaires. By analyzing this data we were able to determine the factors that project managers thought caused complexity in projects. A new dimension was also added by analyzing it sector-wise. Since we collected data from two different sources, via interviews and through questionnaires, it gave us the opportunity to triangulate the findings. Wesincerely hope that this piece of work will pave way for future research on similar areas like models of complexity and perception of complexity in project management</p>

In the last three decades, complexity theory has gained a lot of importance in several scientific disciplines like astronomy, geology, chemistry etc. It has slowly extended its usage in the field of project management. While trying to understand the managerial demands of modern day projects and the different situations faced in projects, the term ‘complexity’ is progressively becoming a benchmark term. In the recent past some of the challenging projects that have been completed are the Heathrow Terminal 5 and the construction of venues for the Beijing Olympics. But can we call these projects complex?It is probably too simplistic to classify projects as complex or non-complex. What is particularly important is to identify the source of the complexity, the level and also the implications of the complexity. Several academicians have studied the different dimensions and established different classifications of complexity. These are put together into models of complexity.But is this classification well-grounded in reality? This is what we aim to explore through this research. The specific questions that we wish to explore by conducting this research are: How does the understanding of project complexity in actuality conform to the theoretical complexity models? In an effort to answer the primary question, our study will also throw some light on factors of complexity across different sectors. We hope that this distinction will pave way for further research within these sectors. This now brings us to our sub-question:- How do the factors that contribute to complexity compare across different sectors?At the outset of this research, the literature on complexity was reviewed. An attempt was made to understand what complexity means with a focus on the field of project management.It was observed that there is a new wave of thinking in this field and a camp which believes that regular project management tools and techniques cannot be used for complex projects. This has drawn several academicians to generate models of complexity based on various factors. In this research we have focused on some important models like that of Turner and Cochrane, Ralph Stacey, Terry Williams, Kahane and Remington and Pollack. We have tried to see if any of these models fit in with how practitioners understand complexity.To find out how practitioners comprehend complexity, we followed a grounded theory approach and also used quantitative methods to supplement the results in accordance in a mixed methodology. Semi-structured interviews were carried out with nine project managers from different sectors and different  geographical locations. The interviews were analyzed and the data was broken down to different categories referred to as open coding where labelling was done. This was followed by Axial coding where we describe the properties and build relations between these categories. The final stage is selective coding where the emerged theory is integrated and refined.Quantitative data was collected through a short questionnaire which listed out some factors which could cause or lead to complexity in projects. A total of 29 responses were obtained for the questionnaires. By analyzing this data we were able to determine the factors that project managers thought caused complexity in projects. A new dimension was also added by analyzing it sector-wise. Since we collected data from two different sources, via interviews and through questionnaires, it gave us the opportunity to triangulate the findings. Wesincerely hope that this piece of work will pave way for future research on similar areas like models of complexity and perception of complexity in project management

No description available.

Late-successional forests provide a unique opportunity to explore adaptive management approaches that mitigate atmospheric carbon dioxide levels through carbon storage while also enhancing ecological resilience to novel climate and disturbances. Typical benchmarks for adaptive forest management include species diversity and structural complexity, which are widely considered to increase ecosystem stability and productivity. However, the role of functional trait diversity (e.g., variation in leaf and stem traits) in driving forest productivity and ecosystem resilience remains underexplored. We leveraged existing continuous forest inventory (CFI) data and collected local functional trait observations from CFI plots within late-successional forests in western Massachusetts to explore links between aboveground carbon storage and different types of forest diversity. We then fit a linear model within a Bayesian hierarchical framework applying functional diversity, species diversity, and structural complexity as predictors of live aboveground biomass (AGB) within CFI plots. Our framework integrates local functional trait information with database species mean trait values using a multivariate structure to account for inherent trait syndromes and estimate functional diversity in each plot. Across 626 plot-timepoints, we found that integrating individual functional trait information from co-located plots yielded the best predictions of live AGB. Contrary to expectations, functional diversity had a negative relationship with live AGB. Whereas plots with low functional diversity and higher AGB were dominated by mid-to-late successional hardwood species, plots with high functional diversity had more shade-intolerant species and lower AGB mediated by recent small-scale disturbances. Our results reveal an ontogenetic shift in the effects of functional diversity on AGB productivity over the course of succession in northeastern temperate forests. Corroborating with classical models of biomass development in late-successional northern hardwood forests, our findings support the need for adaptive forest carbon management to facilitate a mosaic of different forest successional stages across the landscape to maximize live aboveground carbon benefits in northeastern mixed hardwood forests.

Temperate forests are an important carbon sink, yet there is uncertainty regarding land-use history effects on biomass accumulation and carbon storage potential in secondary forests. Understanding long-term biomass dynamics is important for managing forests as carbon sinks and for co-benefits such as watershed protection and biodiversity. However there are many unanswered questions regarding these dynamics in northeastern U.S. forests: How have secondary forests of the U.S. Northeast recovered post nineteenth century agricultural abandonment? How has the region's extensive land-use history influenced long-term structural development and aboveground carbon storage? To answer these questions, we employed a longitudinal study based on twelve years of empirical data (2001-2013) from the Marsh-Billings-Rockefeller (MBR) National Historical Park in Woodstock, VT. MBR Park was the first parcel of land to actively be reforested in the eastern U.S., and as such, its diverse forest mosaic reflects a history of alternate reforestation approaches and varied successional trajectories indicative of secondary forest recovery occurring across the broader northeastern forest landscape. We also used 150 years of documentary data from park management records. This research evaluates the effects of reforestation approaches (planting vs. natural regeneration), management regimes (long-term low-to-intermediate harvest intensities at varied harvest frequencies), and stand development pathways on biomass outcomes. We generated biometrics representative of stand structural complexity, including the H' structural diversity index, and aboveground biomass (live trees, snags, and downed coarse woody debris pools) estimates. Multivariate analyses evaluated the predictive strength of reforestation approach, management history, and site characteristics relative to aboveground carbon pools and stand structural complexity. Classification and Regression Tree (CART) analysis ranked reforestation approach (plantation or natural regeneration) as the strongest predictor of long-term mean total aboveground carbon storage, while harvest frequency, and stand age were selected as secondary variables. CART ranked forest percent conifer (a metric closely associated with reforestation approach) as the strongest predictor of H' index, while harvest intensity, and harvest frequency were selected as secondary variables. Increases in harvest intensity can significantly reduce aboveground carbon storage. Our results suggest that a variety of long-term recovery pathways converge on high levels of aboveground carbon storage, including both conifer plantations and naturally regenerated hardwood stands, but choice of silvicultural management approach can dramatically alter those trajectories. Importantly, total aboveground biomass (i.e., carbon) co-varied with H' (r2 = 0.25), and thus, our dataset showed a positive relationship between forest carbon storage and structural complexity, supporting the concept of multifunctional forestry emphasizing late-successional habitats.

Shallow sheltered Baltic Sea bays are ecologically important habitats that harbour a unique vegetation community and constitute vital reproduction areas for many coastal fish species. Knowledge about the invertebrate community in these bays is, however, limited. This thesis examines the macroinvertebrate community in shallow sheltered Baltic Sea bays and how it is affected by: (1) the natural morphometric isolation of bays from the sea due to post-glacial land uplift; and (2) differences in vegetation types. The invertebrate biomass and number of taxa was found to decrease with increased bay isolation. The taxon composition changed from dominance by bivalves and gastropods in open bays to a community composed of a larger proportion of insects in isolated bays. Stable isotope analysis indicated epiphytes and periphyton as the major energy resources for most of the examined consumers, but the relative importance of these in relation to larger plants decreased for some consumers with increased bay isolation. A comparison of invertebrate abundance between plants revealed a close relationship with morphological complexity of the plants. More complexly structured plants had higher invertebrate abundance than plants with simpler morphology. The results suggest that management of these coastal habitats should be dynamic and take into consideration the natural change in invertebrate community resulting from the slow bay isolation process. In addition, the results imply that changes in the aquatic vegetation due to anthropogenic influences could induce changes in the invertebrate community as the plant habitat structure is altered. A changed invertebrate community may in turn affect higher trophic levels since invertebrates are important food for many fish and waterfowl species. / At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Submitted. Paper 4: In press.