Diversity Patterns of Amphibians in Lowland Amazonian Forests in Southeastern Peru

Biological diversity is threatened worldwide and it is a priority to generate more information that can be used both for understanding ecological processes and determining conservation strategies. For my dissertation, I focused on amphibian diversity patterns in lowland rainforests of southwestern Amazonia to evaluate the importance of habitat heterogeneity in the region. My main purpose was to test the hypothesis that amphibian communities in different forest types differ in species richness, composition, and abundance. I used standardized visual encounter surveys to quantify the species composition and abundance of amphibians at four sites, each containing four forest types (floodplain, terra firme, bamboo, and palm swamp). I used leaf-litter plots to evaluate the effect of soil and leaf-litter characteristics on species richness and abundance of leaf-litter frogs. I intensively sampled at one site and then sampled three other sites (distance among sites varied 3.5-105 km) to evaluate whether the patterns observed at one site were similar elsewhere. I also updated the information on threatened and potentially threatened amphibians in Peru and my study region. I found that no species appears to have experienced population declines in southeastern Peru, suggesting that the region still contains the original species pool. My results support the hypothesis that amphibian communities differ across forest types and that patterns observed at the local scale (one site) are similar at the regional scale (four sites). My data also indicate that there is no correlation between species composition and geographic distance among sites. Instead, an important proportion of the gamma diversity is represented by habitat-related beta diversity. My leaf-litter plot data showed that part of the variation in the leaf-litter community structure is explained by soil and litter characteristics. I found that soil total phosphorus and, to a lesser extent, humidity, leaf-litter mass, and pH is linked to species presence/absence and abundance. My study provides the first standardized, quantitative comparison of amphibian community structure across four major forest types in southwestern Amazonia and highlights the fact that forest types are complementary and necessary for maintaining high species richness in the region.

Mitigating inconsistencies by coupling data cleaning, filtering, and contextual data validation in wireless sensor networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.