Abstract
In recent decades, the Rocky Mountains (RM) have undergone significant changes associated with anthropogenic activities and natural disturbances. These changes have the potential to alter primary productivity and biomass carbon storage. In particular, dissolved organic carbon (DOC) in RM streams can affect heterotrophic processes, act as a source for the nutrient cycle, absorb sunlight radiation, alter metal transport, and can promote the production of carcinogenic byproducts during water treatment. Recent studies have focused on the relationship between bark beetle infestations and stream organic matter but have reached conflicting conclusions. Consequently, here we compile and process multiple datasets representing features of the RM for the period 1983–2012 with the purpose of assessing their relative influence on stream DOC concentrations using spatial statistical modeling. Features representing climate, land cover, forest disturbances, topography, soil types, and anthropogenic activities are included. We focus on DOC during base-flow conditions in RM streams because base-flow concentrations are more representative of the longer-term (annual to decadal) impacts and are less dependent on episodic, short-term storm and runoff/erosion events. To predict DOC throughout the network, we use a stream network model in a 56,550 km2 area to address the intrinsic connectivity and hydrologic directionality of the stream network. Natural forest disturbances are positively correlated with increased DOC concentrations; however, the effect of urbanization is far greater. Similarly, higher maximum temperatures, which can be exacerbated by climate change, are also associated with elevated DOC concentrations. Overall, DOC concentrations present an increasing trend over time in the RM region.
Highlights
Anthropogenic activities have highly influenced ecosystems in the Rocky Mountains (RM)
The first step in the implementation of a Least Absolute Shrinkage and Selection Operator (LASSO) regression is the selection of λ, which is the parameter that determines how strong the penalty is for the model coefficients
Leave-one-out cross-validation tends to overfit the data for big sample sizes, as is the case here; 10-fold cross-validation produces results that may vary depending on the partitioning of the sample; and generalized cross-validation does not withhold any of the data while fitting but instead includes a penalty to compensate for the fact that the same observations are used in fitting and predicting [66,67,68]
Summary
Urbanization and forest logging are clear examples of direct impact (e.g., [1,2,3,4,5]). Climate change has provided optimal conditions for fires and mountain pine beetle (MPB) outbreaks, which can lead to hydrologic disturbances. The MPB is an autochthonous bark beetle species from the RM, and it has shifted its impacts from periodic and localized outbreaks to widespread infestations (e.g., [6,7,8,9]). Fire management policies have resulted in an overall increase in and homogenization of the forest age, making pine forests more vulnerable to bark beetles [10,11]. During the last two decades, bark beetle infestations have affected more than 5 million ha of forest in the western U.S and British
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