In Maryland, freshwater streams integrate the impacts of urbanization, agriculture, and deforestation within watersheds and transmit these disturbances as runoff, heat, and sediment to the Chesapeake Bay. Because anthropogenic disturbances alter stream community composition and often reduce biodiversity, biotic inventories of stream communities routinely are used to inform resource management and to assess the structure and function of stream ecosystems.
Considering the expense of field sampling, the coverage of biotic inventories typically is sparse relative to the area of management concern. Conservation plans therefore must often rely on either coarse extrapolation of biological attributes to watersheds or on some other, usually environment-based, stream classification scheme based on broad-scale environmental variables thought to be functionally related to, or correlated with, biological variation. A reliance on broad-scale environmental drivers comes at the exclusion of reach-scale local factors and can obscure fine-scale attributes of stream networks that support unique biological assemblages. In addition, stream classifications rarely consider the landscape context of streams and in particular, the role of stream connectivity in determining community composition.
In this project, we are combining novel statistical techniques for modeling the composition of biological communities with methodological advances in landscape ecology that facilitate the development of environmental parameters that describe both local and landscape-scale characteristics of individual stream reaches. We will use these methods to quantify and map biological composition of Maryland streams at high spatial resolution.
Task 1: Develop environmental and stream burial datasets
Our goal is to assemble novel spatial continuous representations of land cover, topographic, and parameters derived from ongoing work at AL on stream burial at a range of scales for modeling stream community composition. At every sampling location from the Maryland Biological Stream Survey (MBSS) and for every 10-meter reach of stream habitat, we will develop a comprehensive set of topographic, land cover, stream burial, and environmental variables that acknowledge both local- and landscape-scale conrols on aquatic species composition as well as the hierarchical nature of stream networks.
Task 2: Estimate connectivity of stream reaches
Using new methods to quantify connectivity of stream reaches, we will assess the differential importance of connectivity and environment in determining community composition of fish and macroinvertebrates.
Task 3: Develop models of aquatic community composition
We will develop a biologically-optimized environmental classification of Maryland streams as a function of both environment and connectivity using novel statistical approaches. These methods will allow us to predict composition of fish and macroinvertebrates as a function of our novel environmental predictors and connectivity measures. The output will be used to map fish and macroinvertebrate community types and to quantify group similarity.