The intertidal zone - the region between the low and high tide lines along the coasts of the world?s oceans - serves as a key test bed for exploring the effects of global climate change on species distributions and abundances. Recent studies show that the first signs of climate change involve not only mortality but also changes in the growth and reproductive output of organisms. Therefore, the ability to quantify spatial and temporal patterns of physical stressors in nature is critical, in order to connect laboratory measurements of physiological tolerance to ecological patterns in nature. Many large-scale remote monitoring networks (buoys, satellites, weather stations) have been deployed to collect environmental data (e.g. air, water and surface temperature) across broad spatial scales for extended periods of time, with the goal of relating climate change to the present and future distributions of populations and species. While large-scale measurements of these parameters are vital, they may not be sufficient for understanding and predicting the effects of weather and climate change on patterns of distribution and changes in biodiversity, because large-scale measurements of "habitat" are often very poor predictors of "the environment" as perceived by the organism. For example, air temperature is often a very poor indicator of animal body temperature, which is what drives physiological response. We propose to design and deploy a wireless "biomimetic" sensor network (WSN) that monitors environmental parameters most relevant to climate change impacts- temperature and pH- at temporal and spatial scales that are physiologically and ecologically relevant. Despite the wide varieties of WSNs that have been studied and deployed in various terrestrial environments (e.g., forests, deserts, swamps, volcanoes), monitoring the intertidal zone remains challenging. Periodic changes in sea level due to tides cause the communication channel quality to oscillate and thus make the intertidal zone a highly unstable environment for radio communication. To survive in such an environment, we propose a channel-aware sensor network that can self-diagnose its communication capability and adapt its network protocols accordingly, e.g., an adaptive MAC protocol has various transmission policies depending on the channel quality.
In addition to the broader implications of our study for climate change to society, we propose an integrative plan to form teams of teachers, undergraduate and graduate students. These teams will collaboratively conduct research, and will produce inquiry- and, standards-based educational materials for local K-12 classrooms based on our study results, emphasizing the applications of cyber-enabled technology for studying the natural world, and the effects of weather and climate in particular.
The ultimate goal is to study physiological responses to temperature and pH, and the ecological effects of weather and climate. This proposal will (1) achieve a fundamental advance in understanding the ecological impacts of climate change by monitoring the small-scale, local conditions of ecologically important animals (mussels) in near-real time using wireless sensor networks, and produce a mechanism for creating an "early warning system" for key intertidal sites (e.g., reserves, shellfish farms) around the world, (2) achieve fundamental advances in channel-aware wireless sensor networks that can intelligently estimate the communication channel quality and select opportunities for uploading measurements to a data collection center in a highly unstable radio environment. In addition to monitoring the environmental parameters of interest, the network will sense properties that directly affect the communication channel condition and achieve channel-awareness.
