Climate change is expected to alter temperature and precipitation patterns on military lands throughout the western U.S., and this will alter the timing, frequency, and magnitude of flood and drought events. These changes in streamflow regime will directly affect populations of aquatic organisms (fish, aquatic invertebrates, riparian vegetation) and indirectly affect stream-dependent birds, reptiles, and mammals, including federally threatened and endangered species and other at-risk species. Although climate models as drivers of hydrologic models are becoming increasingly sophisticated in their ability to enable forecasting changes in streamflow regime at small spatial scales (e.g., <144 km2), current species population models do not accommodate the non-stationary effects that shifting flow regimes can exert on population trajectories and viability. Thus, a critical gap remains between our ability to model how climate change will alter streamflow regimes and our ability to predict how these changes will impact management-sensitive aquatic and riparian organisms. The objective of this proposal is to fill this knowledge gap by designing, testing, and implementing flow-population models that integrate nonstationary flow regime dynamics with quantitative population models to forecast potential impacts on aquatic and riparian taxa.

This project will combine the: (1) development of novel flow-population models for riparian vegetation, fish, and aquatic invertebrates; (2) parameterization of model vital rates using longterm datasets from military and other lands; (3) and implementation of models to forecast how changing climate regimes will affect aquatic populations across a suite of western U.S. military installations. Objective 1 will build flow-population models that are suitable for capturing the non-stationary, stochastic dynamics that flow regimes exert on populations. Importantly, a “flow-response guild” approach will be followed, in which groups of species that share similar responses to flow regime attributes will be modeled. Objective 2 involves the parameterization of models using data from aridland military installations and other long-term sites. This data-intensive objective will enable testing of model predictions and calibration of models so they directly apply to groups of species occurring on aridland Department of Defense installations. Objective 3 uses the modeling approach to forecast the ecological effects of changing streamflow regimes. This objective will explore future climate change scenarios and their effects on aquatic and riparian organisms.

Flow-population models for fish and riparian vegetation were constructed using coupled matrix population models, while the aquatic invertebrate model utilized a time-varying form of logistic population growth. Models were parameterized using vital rates obtained from the literature and from our study sites located on Camp Pendleton and Fort Hunter Liggett (CA), Fort Huachuca (AZ), and Pinon Canyon Maneuver Site (CO). Testing of model performance against known datasets showed that the models were capable of recovering empirical patterns of population trajectories. Using network analysis, we found that the riparian vegetation model could be used to identify potentially important species interactions across a range of flow regimes, including future flow regime scenarios that could arise under nonstationary climate change. Analysis of the three models simultaneously revealed important tradeoffs between optimal flow regime for any single taxonomic group (vegetation, fish, or invertebrates) and the other two groups.

The research will provide a critical link between landscape-level climate predictions and population responses of organisms. This link will enable researchers and managers to anticipate how climate-driven changes to precipitation will change current distributions of aquatic and riparian organisms. The project also will produce ready-to-use web-based tools for managers that will enable them to explore the consequences of proposed management actions on relevant flow-response guilds, without requiring direct mastery of the underlying mathematical models.