Stable isotopes reveal complex changes in trophic relationships following nutrient addition in a coastal marine ecosystem

Complex links between the top-down and bottomup forces that structure communities can be disrupted by anthropogenic alterations of natural habitats.We used relative abundance and stable isotopes to examine changes in epifaunal food webs in seagrass (Thalassia testudinum) beds following 6 months of experimental nutrient addition at two sites in Florida Bay (USA) with different ambient fertility. At a eutrophic site, nutrient addition did not strongly affect food web structure, but at a nutrient-poor site, enrichment increased the abundances of crustacean epiphyte grazers, and the diets of these grazers became more varied. Benthic grazers did not change in abundance but shifted their diet away from green macroalgae + associated epiphytes and towards an opportunistic seagrass (Halodule wrightii) that occurred only in nutrient addition treatments. Benthic predators did not change in abundance, but their diets were more varied in enriched plots. Food chain length was short and unaffected by site or nutrient treatment, but increased food web complexity in enriched plots was suggested by increasingly mixed diets. Strong bottom-up modifications of food web structure in the nutrient-limited site and the limited top-down influences of grazers on seagrass epiphyte biomass suggest that, in this system, the bottom-up role of nutrient enrichment can have substantial impacts on community structure, trophic relationships, and, ultimately, the productivity values of the ecosystem.

Periphyton light transmission relationships in Florida Bay and the Florida Keys, USA

Light transmission was measured through intact, submerged periphyton communities on artificial seagrass leaves. The periphyton communities were representative of the communities on Thalassia testudinum in subtropical seagrass meadows. The periphyton communities sampled were adhered carbonate sediment, coralline algae, and mixed algal assemblages. Crustose or film-forming periphyton assemblages were best prepared for light transmission measurements using artificial leaves fouled on both sides, while measurements through three-dimensional filamentous algae required the periphyton to be removed from one side. For one-sided samples, light transmission could be measured as the difference between fouled and reference artificial leaf samples. For two-sided samples, the percent periphyton light transmission to the leaf surface was calculated as the square root of the fraction of incident light. Linear, exponential, and hyperbolic equations were evaluated as descriptors of the periphyton dry weight versus light transmission relationship. Hyperbolic and exponential decay models were superior to linear models and exhibited the best fits for the observed relationships. Differences between the coefficients of determination (r2) of hyperbolic and exponential decay models were statistically insignificant. Constraining these models for 100% light transmission at zero periphyton load did not result in any statistically significant loss in the explanatory capability of the models. In most all cases, increasing model complexity using three-parameter models rather than two-parameter models did not significantly increase the amount of variation explained. Constrained two-parameter hyperbolic or exponential decay models were judged best for describing the periphyton dry weight versus light transmission relationship. On T. testudinum in Florida Bay and the Florida Keys, significant differences were not observed in the light transmission characteristics of the varying periphyton communities at different study sites. Using pooled data from the study sites, the hyperbolic decay coefficient for periphyton light transmission was estimated to be 4.36 mg dry wt. cm−2. For exponential models, the exponential decay coefficient was estimated to be 0.16 cm2 mg dry wt.−1.

Employee Orientation: An Organizational and Experiential Learning Approach

Employee orientation problems for a resort chain were studied and addressed through action research. The implemented solution leveraged experiential learning to foster employee initiative and problem solving to instill a culture of learning, improve customer satisfaction and increase employee retention. Business results were achieved but learner/ management reaction was mixed.

Time orientation in organizations: Polychronicity and multitasking

This dissertation consists of four studies examining two constructs related to time orientation in organizations: polychronicity and multitasking. The first study investigates the internal structure of polychronicity and its external correlates in a sample of undergraduate students (N = 732). Results converge to support a one-factor model and finds measures of polychronicity to be significantly related to extraversion, agreeableness, and openness to experience. The second study quantitatively reviews the existing research examining the relationship between polychronicity and the Big Five factors of personality. Results reveal a significant relationship between extraversion and openness to experience across studies. Studies three and four examine the usefulness of multitasking ability in the prediction of work related criteria using two organizational samples (N = 175 and 119, respectively). Multitasking ability demonstrated predictive validity, however the incremental validity over that of traditional predictors (i.e., cognitive ability and the Big Five factors of personality) was minimal. The relationships between multitasking ability, polychronicity, and other individual differences were also investigated. Polychronicity and multitasking ability proved to be distinct constructs demonstrating differential relationships with cognitive ability, personality, and performance. Results provided support for multitasking performance as a mediator in the relationship between multitasking ability and overall job performance. Additionally, polychronicity moderated the relationship between multitasking ability and both ratings of multitasking performance and overall job performance in Study four. Clarification of the factor structure of polychronicity and its correlates will facilitate future research in the time orientation literature. Results from two organizational samples point to work related measures of multitasking ability as a worthwhile tool for predicting the performance of job applicants.

Mixed spatial and nonspatial problems in location based services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

Time Orientation in Organizations: Polychronicity and Multitasking

This dissertation consists of four studies examining two constructs related to time orientation in organizations: polychronicity and multitasking. The first study investigates the internal structure of polychronicity and its external correlates in a sample of undergraduate students (N = 732). Results converge to support a one-factor model and finds measures of polychronicity to be significantly related to extraversion, agreeableness, and openness to experience. The second study quantitatively reviews the existing research examining the relationship between polychronicity and the Big Five factors of personality. Results reveal a significant relationship between extraversion and openness to experience across studies. Studies three and four examine the usefulness of multitasking ability in the prediction of work related criteria using two organizational samples (N = 175 and 119, respectively). Multitasking ability demonstrated predictive validity, however the incremental validity over that of traditional predictors (i.e., cognitive ability and the Big Five factors of personality) was minimal. The relationships between multitasking ability, polychronicity, and other individual differences were also investigated. Polychronicity and multitasking ability proved to be distinct constructs demonstrating differential relationships with cognitive ability, personality, and performance. Results provided support for multitasking performance as a mediator in the relationship between multitasking ability and overall job performance. Additionally, polychronicity moderated the relationship between multitasking ability and both ratings of multitasking performance and overall job performance in Study four. Clarification of the factor structure of polychronicity and its correlates will facilitate future research in the time orientation literature. Results from two organizational samples point to work related measures of multitasking ability as a worthwhile tool for predicting the performance of job applicants.

Mixed Spatial and Nonspatial Problems in Location Based Services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.