title: Linking river flow regimes to riparian plant guilds: a community-wide modeling approach.
abstract: Modeling riparian plant dynamics along rivers is complicated by the fact that plants have different edaphic and hydrologic requirements at different life stages. With intensifying human demands for water and continued human alteration of rivers, there is a growing need for predicting responses of vegetation to flow alteration, including responses related to climate change and river flow management. We developed a coupled structured population model that combines stage-specific responses of plant guilds with specific attributes of river hydrologic regime. The model uses information on the vital rates of guilds as they relate to different hydrologic conditions (flood, drought, and baseflow), but deliberately omits biotic interactions from the structure (interaction neutral). Our intent was to (1) consolidate key vital rates concerning plant population dynamics and to incorporate these data into a quantitative framework, (2) determine whether complex plant stand dynamics, including biotic interactions, can be predicted from basic vital rates and river hydrology, and (3) project how altered flow regimes might affect riparian communities. We illustrated the approach using five flow-response guilds that encompass much of the river floodplain community: hydroriparian tree, xeroriparian shrub, hydroriparian shrub, mesoriparian meadow, and desert shrub. We also developed novel network-based tools for predicting community-wide effects of climate-driven shifts and deliberately altered flow regimes. The model recovered known patterns of hydroriparian tree vs. xeroriparian shrub dominance, including the relative proportion of these two guilds as a function of river flow modification. By simulating flow alteration scenarios ranging from increased drought to shifts in flood timing, the model predicted that mature hydroriparian forest should be most abundant near the observed natural flow regime. Multiguild sensitivity analysis identified substantial network connectivity (many connected nodes) and biotic linkage (strong pairwise connections between nodes) under natural flow regime conditions. Both connectivity and linkage were substantially reduced under drought and other flow-alteration scenarios, suggesting that community structure is destabilized under such conditions. This structured population modeling approach provides a useful tool for understanding the community-wide effects of altered flow regimes due to climate change and management actions that influence river flow regime.
authors.affiliation: Department of Integrative Biology, Oregon State University, Corvallis, Oregon, 97331, USA.
authors.forename: David A
authors.initials: DA
authors.lastname: Lytle
authors.affiliation: Watershed, Fisheries, Wildlife, Air and Rare Plants Staff and the National Stream and Aquatic Ecology Center, USDA Forest Service, Fort Collins, Colorado, 80526, USA.
authors.forename: David M
authors.initials: DM
authors.lastname: Merritt
authors.affiliation: Department of Integrative Biology, Oregon State University, Corvallis, Oregon, 97331, USA.
authors.forename: Jonathan D
authors.identifier: 0000-0002-6053-291X
authors.initials: JD
authors.lastname: Tonkin
authors.affiliation: School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, 98195, USA.
authors.forename: Julian D
authors.initials: JD
authors.lastname: Olden
authors.affiliation: Watershed, Fisheries, Wildlife, Air and Rare Plants Staff and the National Stream and Aquatic Ecology Center, USDA Forest Service, Fort Collins, Colorado, 80526, USA.
authors.forename: Lindsay V
authors.initials: LV
authors.lastname: Reynolds
mesh_terms: D058448:Biota; D057231:Climate Change; D003120:Colorado; D055864:Droughts; D062070:Hydrology; D008954:Models, Biological; D018521:Plant Physiological Phenomena; D011157:Population Dynamics; D045483:Rivers; D014872:Water Movements
keywords: coupled matrix model; flow regime; flow response guild; interaction neutral; neutral theory; riparian community; stochastic population model