The application of metacommunity theory to the management of riverine ecosystems

Abstract

River managers strive to use the best available science to sustain biodiversity and ecosystem function. To achieve this goal requires consideration of processes at different scales. Metacommunity theory describes how multiple species from different communities potentially interact with local-scale environmental drivers to influence population dynamics and community structure. However, this body of knowledge has only rarely been used to inform management practices for river ecosystems. In this paper, we present a conceptual model outlining how the metacommunity processes of local niche sorting and dispersal can influence the outcomes of management interventions and provide a series of specific recommendations for applying these ideas as well as research needs. In all cases, we identify situations where traditional approaches to riverine management could be enhanced by incorporating an understanding of metacommunity dynamics. A common theme is developing guidelines for assessing the metacommunity context of a site or region, evaluating how that context may affect the desired outcome, and incorporating that understanding into the planning process and methods used. To maximize the effectiveness of management activities, scientists and resource managers should update the toolbox of approaches to riverine management to reflect theoretical advances in metacommunity ecology.

Keywords: biomonitoring; conservation; dispersal; invasive species; local sorting; network connectivity; restoration; spatial.

Figure 1.

Conceptual diagram of the factors driving metacommunity dynamics and impacts on various aspects of stream and river management. A. Regional scale factors (dotted box, cream text boxes) affect connectivity among habitats which interacts with dispersal ability of resident organisms to influence the total amount of dispersal among habitats. Dispersal among habitats has a unimodal relationship with local sorting strength, defined as the amount of variation in community composition that local environmental variables can explain. Sorting strength is predicted to have positive relationships with B. biomonitoring precision (inset: electrofishing for aquatic life uses assessment), C. restoration success (inset: examples of restoration projects changing channel morphology), and D. conservation success (inset: individual enjoying ecosystem services provided by a healthy river). In contrast, local sorting is predicted to have a negative relationship with E. habitat invasibility when regional processes are not considered (inset: examples of non-native taxa, rusty crayfish (Orconectes rusticus) and western mosquitofish (Gambusia affinis). Arrow colors denote the sign of the expected relationship among factors (red = negative, green = positive) with gradient arrow indicating non-linear relationships. Inset graphs display the functional form of the expected relationships.

Figure 2.

A) An endangered Japanese Giant salamander (Andrias japonicus) blocked by a dam in the Kiso River drainage in Gifu Province, Japan. Courtesy of Ito Yoshihiro. B) Restoring flow connectivity to the Santa Ana River has been vital to the survival of the endangered Santa Ana Sucker (Catostomus santaanae). C) Following the restoration of flows and native fish communities in Fossil Creek, AZ, a dam was constructed on the lower creek to prevent the recolonization of invasive fish from the downstream Verde River. Natural materials were included in the design of the fish barrier in order to preserve wilderness characteristic and recreational value.

Figure 3.

Decision tree for incorporating metacommunity theory into management activities. 1) Work begins with surveys or statistical analysis of existing biomonitoring datasets to determine the spatial context of the proposed project. This may include assessing factors like connectivity, dispersal ability of organisms, regional species pools, and the relative importance of local sorting for target communities. 2) Determine whether the current spatial context will prevent management goals from being met. For example, a stream restoration in a site that is under strong regional control (dispersal) from degraded sites is unlikely to result in the assembly of desirable biotic community after the restoration is complete. 3) If there are no problems presented by spatial context, proceed with management plan (3a); however, if there are issues managers can either attempt to change the spatial context (3b-1), change the plan (3b-2), or move the project location (3b-3). In each of these cases (3b-1–3), the managers must then return to step 1 or step 2. For example, if spatial context is manipulated by removing barriers to increase connectivity, post-removal conditions should be assessed to determine how much the manipulation changed connectivity. This is a crucial step before proceeding to 3a because not all manipulations may be successful.

Figure 4.

Spatial distribution of sorting strength measured at regional and local scales. A. Map of local sorting strength estimated for regions and mapped at the HUC4 basin scale across the continental United States. Darker blue sites have higher sorting strength. Black areas have no available data. B-E Relationships between different estimates of sorting strength and latitude and longitude across the continental United States. Regional scale estimates of sorting strength increase slightly as you move north (B., P < 0.001, R² = 0.022, df = 2,1220) and toward the coasts (C., P < 0.001, R² = 0.196, df = 2,1220) with the highest values in the Pacific Northwest and the Atlantic Northeast. Local scale estimates have no relationship with latitude (D.) or longitude (E.). F. Example map of spatial distribution of local sorting strength estimated at the site scale for streams with biomonitoring data in the lower Appalachian Mountains of the United States. Sorting strength is ranked as high (red), medium (violet), or low (blue). In this region, sorting strength is lower in upstream reaches.

Figure 5.

Relationships between stream size, ecoregion, and strength of local sorting. A. Distribution of the deviation from expected community based on species-habitat associations among ecoregions of the United States (CPL = Coastal Plains, NAP = Northern Appalachians, NPL = North Plains, SAP = Southern Appalachians, SPL = Southern Plains, TPL = Temperate Plains, UMW = Upper Midwest, WMT = Western Mountains, XER = Xeric). Values with a magnitude greater than zero indicate that communities are not being structured by local environmental variables. B. Map of ecoregions of the United States. Ecoregion colors correspond to colors in other panels. C. Relationship between stream watershed size and community habitat volume deviation within ecoregions (individual lines) of the United States. Left plot shows ecoregions with positive relationships, indicating that, as predicted by theory, sorting strength declines with stream size (NAP, NPL, SPL, WMT, UMW, XER). Right plot shows ecoregions with negative relationships, indicating that sorting strength increases with stream size (CPL, SAP, TPL).