Implementation of landscape perspectives in aquatic ecosystems

Work in aquatic ecosystems began to focus on interactions between aquatic systems and the surrounding landscape at multiple spatial scales as concepts from the field of landscape ecology, such as the influence of spatial scale (Wiens 1989, Levin 1992), spatial and temporal heterogeneity (Pickett and White 1985), boundary dynamics (Naiman and Décamps 1990), and the pattern of landscape elements (Forman and Godron 1986) became more widely accepted beyond terrestrial ecology. The foci of these studies included: 1) the river channel; 2) the river, floodplain, and riparian zone; and 3) the surrounding landscape that interacts with the river through surface and subsurface flow paths (Wang et al. 2006a). Recognition also was growing that the river included the wetted channel, side channel habitats, alluvial wetlands, springs, backwaters, floodplains, and floodplain lakes, and groundwater (Ward et al. 2002a; Fig. 1) and that river habitats were embedded in a shifting mosaic controlled by geology, hydrology, and climate (Arscott et al. 2002). This perspective was further developed by Townsend (1996), Fausch et al. (2002; Fig. 1), Ward et al. (2002a, b), and Wiens (2002; Fig. 1), who each emphasized the interactions between physical processes and spatial patterns and effects on the fluxes of biota, energy, and materials across patches and boundaries through time. Fausch et al. (2002) highlighted the work of Schlosser and colleagues, who pioneered new approaches based on this landscape paradigm. Schlosser and colleagues examined fish behavior in the context of their use of and movement across multiple habitat patches throughout their life cycle and translated their results in the context of disturbance ecology and conservation (Schlosser 1991 [Fig. 1], 1995, Schlosser and Angermeier 1995). This work was especially significant because it linked biotic processes with structural and functional aspects of streams and riparian zones at appropriate scales relevant to stream conservation and management planning (Fausch et al. 2002). We now accept that such approaches explicitly or implicitly incorporate theory and concepts from the disciplines of conservation biology (Ward 1998, Fausch et al. 2002, Strayer and Dudgeon 2010), geomorphology and hydrology (Statzner and Higler 1985, Townsend 1996, Poff et al. 1997, Poole 2002, 2010, Thorp et al. 2006, Dollar et al. 2007), community ecology (Vinson and Hawkins 1998, Ward and Tockner 2001, Malmqvist 2002 [Fig. 1], Clarke et al. 2008, Winemiller et al. 2010), and population biology (Newton et al. 2008).

The NABS conference plenary session in 1995 (and the resulting special issue of Freshwater Biology) was one of the first compilations to focus on the influence of landscape and reach-scale variables on fish, invertebrates, water chemistry, coarse particulate organic matter (CPOM), and other attributes of lotic ecosystems (Allan and Johnson 1997). In a review of the influence of land use on stream ecosystems, Allan (2004) lamented that we have been only moderately successful in quantifying the underlying mechanisms accounting for the observed responses because: 1) covariation between the natural and anthropogenic factors on the landscape is considerable, 2) influences of these natural and anthropogenic factors are multifactorial and span several spatial scales, 3) landuse legacy effects are difficult to separate from present-day influences, and 4) many responses are nonlinear. Many of these topics are addressed below and in a 31-chapter volume on “Landscape Influences on Stream Habitats and Biological Assemblages” (Hughes et al. 2006; Fig. 1).

Classification of aquatic ecosystems

The nomenclature of Frissell et al. (1986) was useful but did not fully account for the diversity and patchiness of hydrological characteristics (flow regime, flow history, and hydraulics) that occur within and across subcatchments and valley segments and that control many properties and processes within the ecosystem (Montgomery 1999, Poole 2002, 2010, Parsons et al. 2003, Benda et al. 2004, Thorp et al. 2006). A classification system based on arrays of patches (hydrogeomorphic patches) formed by discontinuities in geomorphic and hydrologic conditions has been proposed (Thorp et al. 2006). The new field of ecohydrology incorporates tenets of ecology, geomorphology, and hydrology in water resource management (Zalewski et al. 1997, Poole2010) and accounts for these discontinuities.

Lake management and conservation programs increasingly are adopting regional approaches for planning, assessment, and monitoring that use landscape and climate as fundamental predictors of lake properties. Climate, geomorphology, and other landscape variables provide overarching constraints on chemical, physical, and biological attributes of aquatic ecosystems. Understanding these constraints provides a context for assessing system responses to human activities or natural disturbances and predicting assemblage structure. To this end, considerable effort has been devoted to classifying lakes, streams, and wetlands in terms of their internal properties (Hawkes 1975, Cowardin et al. 1979, Busch and Sly 1992) and their position within regional landscapes (Higgins et al. 2005). Such classification systems have not been well tested to determine whether the resulting groups of lakes are coherent in their properties (but see Jenerette et al. 2002). Cheruvelil et al. (2007) tested several regionalization frameworks that classified lakes with similar water chemistry and quality to determine the appropriate spatial scale for effective regional monitoring. The best classification systems were those that resulted in smaller divisions and a larger number of classes. Tests with only minimally disturbed lakes captured less variance among regions (discussed below).

Ecoregional boundaries are based on the coincidence of physiographic features, including topography, land form, soils, and climate (Omernik 1987, Host et al. 1996), and rarely coincide with watershed boundaries. They provide a classification that has the potential to capture the broad-scale climatic and physiographic properties that influence aquatic ecosystems. The 2000 special issue of J-NABS focused on the role of landscape classification in bioassessment and quantifying various aspects of aquatic biota (Hawkins and Norris 2000). Authors examined classification relationships mainly in rivers (Feminella 2000, Gerritsen et al. 2000, Hawkins et al. 2000, Marchant et al. 2000, Rabeni and Doisy 2000, Sandin and Johnson 2000, Van Sickle and Hughes 2000, Waite et al. 2000), but one article focused on Swedish lakes (Johnson 2000). Articles addressed biotic assemblages, including invertebrates (Hawkins and Vinson 2000), diatoms (Pan et al. 2000), and fish (McCormick et al. 2000, Oswood et al. 2000). In a synthesis of these studies, Hawkins et al. (2000) concluded that landscape classifications alone were not sufficiently resolved to benefit bioassessment efforts, although landscape classifications did account for significant variation in biotic communities (Hawkins et al. 2000).

This conclusion was not surprising because factors operating at many scales determine the communities or ecosystem properties of an individual water body. This result led to refinements of ecoregion classification, in which additional landscape variables associated with particular biota were included in the classification framework. For example, the River Environment Classification for New Zealand (Snelder et al. 2004) includes climate, topography, geology, land cover, and network structure as classification variables. In a comparison of 5 classification systems based on combinations of ecoregions, geology, digital elevation models, or the geographical distance between sites, Pyne et al. (2007) agreed with Snelder et al. (2004) that different combinations (and scales) of classification variables were needed to predict attributes of different assemblages. The results of this and other studies suggest that regional patterns in biotic assemblages can be predicted from broad-scale classification systems, but site-level patterns are controlled by local-scale features (Hawkins and Vinson 2000, Van Sickle and Hughes 2000, Snelder et al.2004, 2008).

Hierarchical control of habitat structure and communities

The strong hierarchical influence of landscape features (e.g., catchment size, geomorphology) and position in the network on habitat structure (e.g., depth, substrate, water velocity) and biotic assemblages has been acknowledged explicitly for some time (Allan and Johnson 1997). Disentangling the effects of features at different spatial scales has been the topic of many studies (especially of lotic ecosystems) over the past decade (reviewed by Allan 2004). Many studies partitioned variation among factors occurring at 3 to 4 spatial scales (e.g., landscape with ≥1 catchments; stream segment consisting of a valley segment, a shorter stream segment defined by riparian conditions, and a local habitat scale). More researchers are using a network or subnetwork scale that encapsulates conditions within a certain buffer distance of the stream (Burnett et al. 2006, Wang et al. 2006b). Variations in these approaches have been used to examine patterns across very broad areas (nation, state, ecoregions, large drainage basins; Allen et al. 1999, Jones et al. 2001, Johnson et al. 2004, Pan et al. 2004, Sandin and Johnson 2004, King et al. 2005, Arscott et al. 2006, Goldstein et al. 2007), or to include smaller units, such as pool/riffle habitats or microhabitat patches (Townsend et al. 2004, Smiley and Dibble 2008, Winemilleret al. 2010). In nonlotic systems, authors have examined buffers at various distances surrounding wetlands to assess the influence of landscape phenomena (e.g., Mensing et al. 1998, Brazner et al. 2007a, b).

Robust statistical techniques have become available only recently to partition this variation into constituent parts. The singular and combined effects of variables at different spatial scales can be quantified with variance partitioning methods (Richards et al. 1997, Johnson et al. 2004, 2007, Stoffels et al. 2005, Gido et al. 2006, Kratzer et al. 2006, Brazner et al. 2007a, b). Another common approach for assessing effects at various spatial scales is to develop regression models based on groups of variables that represent the different scales, with subsequent comparison of the model fit by comparison of the F-value, variance explained, or evaluation of Akaike information criterion (AIC; Akaike 1974, Dow et al. 2006). Techniques for partitioning variation among stressor and nonstressor variables or among spatial scales are addressed below.

Burnett et al. (2006), Johnson et al. (2006), and Dow et al. (2006) used regression to evaluate the influence of catchment-, riparian-, and local-scale variables on habitat. They found distinct regional differences in the spatial scales characterizing dominant predictors of habitat, a strong influence of geomorphic factors, such as catchment area and channel width, on the distribution of large wood, and significant effects of land use/cover at riparian to subcatchment scales. Goldstein et al. (2007) evaluated the relative importance of environmental setting (geology, topography) vs land use on stream habitat variables across the US and found that land use was not a good predictor of stream habitat variables at the scale of the continental US. However, the predictive power of land use increased as the scale decreased to that of level II ecoregions. Hierarchical control over habitat structure has been addressed in relatively few studies, but an increasingly large body of work has addressed geomorphic controls over the structure and function of large floodplains and river channels (Poole 2010).

Many studies have examined interactions among spatial scales to explain patterns of biotic diversity and assemblage structure (e.g., Corkum and Ciborowski 1988, Corkum 1991 [Fig. 1], 1992 [Fig. 1]), Downes et al. 1993, Boyero and Bailey 2001, Li et al. 2001). As landscape studies have become more common, effort has shifted to address variation in biotic responses (mainly diversity, species richness, and assemblage structure) along environmental or geographic gradients (Table 3) and to test theoretical relationships regarding the processes responsible for controlling local and regional species richness. Winemiller et al. (2010) provide a review of patch-scale phenomena that influence biotic assemblages and populations.

Spatial heterogeneity of riverine systems influences α (within-site) and β (among-site) diversity (Ward 1998, Ward and Tockner 2001, Amoros and Bornette 2002 [Fig. 1], Robinson et al. 2002). Disparities persist with respect to predictions of local vs regional or joint control over fish α diversity (Hugueny and Paugy 1995, Angermeier and Winston 1998, Oberdorff et al. 1998). Local diversity was not associated with regional diversity in 5 Mississippi River drainages (Love and Taylor 2004). Fish assemblage diversity was hierarchically controlled in 2 large tributary catchments of the Mississippi River in Illinois, but disturbance levels influenced diversity patterns (Pegg and Taylor 2007). Patterns of diversity for introduced species differed from those of native species (Angermeier and Winston 1998). Strong seasonal patterns in flow regimes (and habitat structure) in tropical lowland rivers appear to be associated with high α and β fish diversity (Arrington and Winemiller 2004).

Patterns of invertebrate species diversity and richness differ depending on the size of the study streams. In Finnish headwater streams, local macroinvertebrate species richness was linearly related to regional species richness, a result suggesting that local communities were unsaturated and free of local control. Regional factors set the upper limit of local species richness (Heino et al. 2003). Clarke et al. (2008) reviewed macroinvertebrate diversity in headwater streams from around the world and concluded that α diversity in headwater streams was not high, but that β diversity was high within catchments and, thus, contributed to higher regional (γ) diversity. Clarke et al. (2008) also concluded that species richness follows a hump-shaped pattern with increasing stream size, i.e., along the longitudinal gradient. Whether macroinvertebrate diversity was determined by regional or local features in Swedish lakes and streams was not clear (Stendera and Johnson 2005). α diversity (within sites and within ecoregions) was low, whereas β diversity (between sites and between ecoregions) was high, a result suggesting that regional factors were more important than local factors (Stendera and Johnson 2005). That study suggested that the size of the regions studied might explain differences in results between Finnish and Swedish systems. Their study illustrated the importance of using different diversity indices to account for the contributions of rare and common species (see also Erös 2007, Heino et al. 2008). The distributions of individual species are influenced by species interactions and by life-history strategies and landscape connectivity, which play a role in processes, such as dispersal and colonization potential (e.g., Malmqvist 2002). However, spatial and temporal dynamics affecting connectivity and spatial heterogeneity interact to influence biodiversity at multiple scales (Clarke et al. 2008). Such differences highlight the fact that we have not yet arrived at the full explanation of the factors that control diversity and richness. Genetic data are proving essential for quantifying the spatial patterns of species dispersal patterns (Hughes 2007, Finn et al. 2007) and might lead to some resolution of these issues. J-NABS has not played a significant role in this area of research.

Many studies have quantified the relative importance of local and regional variables for predicting assemblage structure. In Table 3, we present a list of representative studies and list the spatial scale of responses for different assemblage types and aquatic ecosystems. The diversity of responses at different scales is notable. One study assessed the spatial scale of responses assemblages of fish, macroinvertebrates, macrophytes, and algae to environmental gradients across relatively undisturbed lowland and mountainous European streams (Johnson et al. 2007). The spatial scales of responses of the 4 assemblage types were unexpectedly similar among assemblages. Most among-site variation was explained by latitude and ecoregion, but when geography was used as a covariate, the greatest variation was explained by local habitat variables reflecting water chemistry and substratum. However, that local habitat largely explains assemblage structure is not universally recognized (Roth et al. 1996, Wang et al. 1997, 2001, Allen et al. 1999, Mykrä et al. 2008; Table 3). Different results among studies might reflect differences in study region size or level (possibly also types) of disturbance. The authors of many studies have concluded that scale dependencies in the distribution of biota should be matched appropriately to the scale dependencies in environmental features to understand most effectively the factors controlling biological communities. Understanding such dependencies can inform the selection of appropriate scales for environmental assessments and the choice of summary metrics (Parsons et al. 2004, Brazner et al. 2007a, b). Furthermore, variability within and among sampling units at different scales should be addressed in the sample design (Heino et al. 2004, Stoffels et al. 2005, Smiley and Dibble 2008) to ensure that observed patterns are not artifacts.

Influence of spatial heterogeneity and context

Landscape position and connectivity, especially with respect to the type, width, and contiguity of riparian vegetation, affect responses of aquatic ecosystems to local and regional conditions (reviewed by Steinman and Denning 2005). However, the influence of the landscape matrix (the dominant land cover in the catchment) in mediating these relationships is not as well understood. Fish assemblage responses were poorly predicted by land use in catchments under intensive agricultural production (resulting in low landscape heterogeneity) in southern Minnesota (Stauffer et al. 2000) and Iowa (Heitke et al. 2006). Macroinvertebrate assemblage structure and species traits also were poorly predicted in predominantly agricultural catchments in Minnesota and Michigan (Richards et al. 1996, 1997). Richards et al. (1996) found that reach-scale (i.e., local) variables were the best predictors of macroinvertebrate species traits in Michigan catchments with a mixed-landuse landscape (higher heterogeneity) and a pronounced disturbance gradient. Over a larger area that included all of Wisconsin and northern Michigan, Wang et al. (2006b) concluded that local-scale variables were the better predictors of fish assemblage composition, abundance, and presence/absence in the more disturbed regions, whereas catchment-scale variables, including land use, were the better predictors in the less disturbed regions. The predictive power of the intermediate-scale variables (network  =  riparian zone upstream of sampling point, and riparian zone within sample reach) was similar among disturbance levels. Hughes et al. (2005) observed a similar shift in importance from local- (instream) to network-scale factors along a gradient from more to less disturbed regions. However, in less-disturbed areas of Michigan, Wisconsin, and Minnesota, which are dominated by forested land cover, landuse variables were better predictors of invertebrate traits than were climate, geology, or reach-scale variables (Weigel et al. 2003).

These studies (and others) suggest that the influence of landscape variables on biotic assemblages is difficult to predict when disturbances are severe and widespread or the landscape is relatively homogeneous, a conclusion that is consistent with the results of nutrient-input simulations by Gergel (2005). This problem probably is attributable to the lack of variation in the landscape predictor variables. Depending on the response variables and the size of the study region, the relative influence of landscape and local variables can be distinguished when the landscape is less disturbed. Across very large regions, variation in biotic assemblages in undisturbed streams often is explained mainly by local features once the effect of latitude has been removed (e.g., Heino 2005, Johnson et al. 2007, Mykrä et al. 2007). However, this trend is not universal (e.g., Gido et al. 2006, Urban et al. 2006, Mykrä et al. 2008), and a better understanding of the interactions among the size of the study region, the amount of background disturbance, and the role of landscape heterogeneity clearly is needed.

The lateral and vertical dimensions of hydrologic connectivity within a catchment or network have been relatively well studied in aquatic ecosystems (Junk et al. 1989, Ward et al. 2002a). Different types and extents (in space and time) of connections influence variables, such as water temperature, turbidity, nutrient content, and habitat heterogeneity. The timing and duration of connections among riverine habitats affect successive creation and destruction of habitats (Arscott et al. 2002) and habitat refugia (Robinson et al. 2002), with resulting patterns in species richness that vary across biota depending on habitat requirements and life-history stage (Tockner et al. 1998). Ecosystem processes, such as production and transport, also influence organic matter processing during various stages of inundation (Amoros and Bornette 2002). Winemiller et al. (2010) discuss resulting patterns of heterogeneity and implications for habitat, biodiversity, and ecosystem processes.

The study of floodplain dynamics has benefitted greatly from the integration of GIS, remote sensing, aerial photograph interpretation, and modelling. A time-series of data collected over 60 y showed that the structure of floodplain systems (i.e., shifting mosaic) was the result of interactions among climate, floods, and plant succession (Whited et al. 2007). Regulation of rivers by humans has profound effects on the geomorphology of a river system because it alters flow regime and sediment deposition (e.g., Surian 1999). These effects also influence riparian (and island) vegetation and other aquatic assemblages (Kollmann et al. 1999, Gurnell et al. 2001). See Bunn and Arthington (2002) for a comprehensive review of the consequences of altered flow regimes for biodiversity, and Stanley et al. (2010) for a more general discussion of disturbance.

Habitat patch connectivity strongly influences biological assemblages. Schlosser (1991, 1995) and Schlosser and Angermeier (1995) were among the first to interpret fish assemblage patterns through the lens of habitat patch connectivity. They recognized that fish life-history requirements and availability of refugia depend on the spatial arrangement of habitat patches within a network. Thus, species-specific life-history and habitat requirements dictate responses to disconnected and connected water bodies. α diversity of fish assemblages tends to decrease, whereas β diversity increases when side-channel habitats become disconnected. In contrast, diversity patterns for selected species of amphibians showed an opposite pattern (Amoros and Bornette 2002). Amphibian diversity in wetland complexes varies with respect to wetland connections to permanent water bodies, which generally support predaceous fish (Wells 2007).

Recognition of the importance of spatial arrangement and spatial scale of observations has fundamentally altered stream fisheries management (Fausch et al. 2002). Spatially continuous data collected at intermediate levels of observation provide the most robust relationship assessments between fish-habitat and habitat heterogeneity (Gresswell et al. 2006, Torgersen et al. 2006). Continuous sampling is expensive and prohibitive for regional efforts, but provides invaluable information on species distributions, population survival, and persistence of communities within a stream network in response to local-scale conditions and disturbances. A less expensive approach is to include spatial data about the position of sampling sites within the stream network implicitly by noting the proximity of a sampling site in a tributary to the main stem of the river (e.g., Hitt and Angermeier 2008). Analyses based on graph theory might be useful for examining the effects of fragmentation on riverine fish populations (Schick and Lindley 2007).

In rivers, landscape position along a regional flow path strongly influences many ecosystem properties, including habitat, water quality, nutrient cycling, and assemblage structure and composition. In lakes, position along a regional flow path influences concentrations of cations and other solutes (Kratz et al. 1997 [Fig. 1], Sorrano et al. 1999, Kling et al. 2000, Lyons et al. 2000), thermal properties (Benson et al. 2000), and structure of algae, invertebrate, fish, snail, or crayfish assemblages (Lewis and Magnuson 2000, Riera et al. 2000, Quinlan et al. 2002, Hrabik et al. 2003, Heino and Muotka 2006). Kratz et al. (1997) reported that fish and snail species richness increased from lakes in the upper drainage to lakes low in the landscape. Low-order lakes are isolated from the regional flow path, and thus reflect the basin and precipitation chemistry. Lakes lower in the chain were more directly influenced by ground and surface water and had higher ionic concentrations and more connections to stream networks than did lakes higher in the chain. Complex chemical and biological responses result from the confounding effects of bedrock and surficial geology, hydrologic regime, and geographic location in both lentic (Sorrano et al. 2009) and lotic (Sweeney et al. 2006) ecosystems.

Natural dispersal barriers limit the connectivity of lakes, and thus, the distribution of fish within lake chains. Landscape tools have been used to help predict the distribution of fish based on identification of such barriers (e.g., Spens et al. 2007). However, factors other than landscape connectivity also influence fish distributions. Landscape variables, such as lake size, depth, outflow gradient, distance to other lakes, lake order, altitude, river drainage, and age of glacial surface were evaluated as predictors of fish distributions and colonization and survival potential in Alaska. The distribution of fish species was influenced by geologic restructuring of the landscape through time that has altered landscape patterns, especially the extent and location of stream connections (Hershey et al. 2006).

Climate changes can alter landscape connectivity to create remnant populations in areas experiencing drying and to increase the potential for colonization in areas experiencing flooding. Enhanced connectivity can allow range extensions of native and of nonnative invasive species. Predicting changes in biotic assemblages under future climate scenarios will be challenging because anthropogenic disturbances that disrupt natural flow regimes (and water levels) and climate effects are likely to be confounded and because the ability to disperse differs among species. Moreover, physicochemical characteristics of water bodies are likely to change as surficial and groundwater connections change.

Landscape perspectives on the impact of disturbance

Human disturbances must be viewed at the catchment scale to account for cumulative impacts and the underlying constraints of the natural landscape. Effects of large-scale phenomena (including human disturbance) on biota can be inferred indirectly through variance partitioning or mechanistic explanations based on habitat responses (e.g., Richards et al. 1996, 1997, Hutchens et al. 2009). Covariance structure analysis (CSA) can quantify direct and indirect landscape effects. CSA was used to analyze effects of catchment hydrology, stream hydraulics, and stream channel shape on fish assemblages, fish biomass, and stream temperature in southern Michigan streams (Infante et al. 2006, Zorn and Wiley 2006, Wehrly et al. 2006). Catchment agriculture was negatively associated with fish condition (mass), but was positively associated with species richness. Catchment variables (stream size, land use, surficial geology) explained substantial spatial variation (50–80%) in habitat variables, such as depth and flow, which directly affected substrate composition. Local-scale variables (nutrients, substrate) had direct effects on fish biomass, whereas catchment-scale variables (area, land use) had indirect effects (Zorn and Wiley 2006). Thus, CSA can provide causal pathways that can be used to identify targets for management and restoration.

Regional impacts of landscape-scale phenomena, such as urbanization, can be quantified more directly when variation caused by local-scale features is removed from the data set by applying rigorous site-selection criteria. A comparative study of 3 urban centers in the US was conducted using this type of design and urban intensity indices (one applied across all regions and others applied to each region) that incorporated land use, socioeconomic factors, and infrastructure variables (Tate et al. 2005). Predictions of habitat (Meador et al. 2005), algae (Potapova et al. 2005), fish (Meador et al. 2005), and macroinvertebrate responses were confounded by strong regional differences among the cities. The best predictors of macroinvertebrate responses were related to land use at the basin scale (Cuffney et al. 2005), a result that was not surprising, given the strong emphasis on use of site-selection criteria to remove variation at the local scale. Moore and Palmer (2005) found strong linear responses of invertebrates to an urban gradient in the Washington, DC, metropolitan area. However, riparian vegetation cover was associated with increased taxonomic diversity, even in areas with significant amounts of impervious surface cover. Alberti et al. (2007) found that the amounts and configurations of impervious surface and forested land were strong predictors of benthic macroinvertebrate assemblages (IBIs). Few other studies have addressed the spatial patterning of landuse elements, although increasing availability of appropriate tools might increase the frequency of such studies.

Disturbances associated with patterns of housing and roads in urbanized landscapes are one of the important landscape elements affecting aquatic ecosystems. Catchment and lakeshore development have been studied at regional scales to assess their impacts on littoral zone habitat structure, including woody debris (Christensen et al. 1996), aquatic vegetation (Radomski and Goeman 2001), trophic interactions (Jeppesen et al. 2000), macroinvertebrates (Brauns et al. 2007), fish distributions, and cultural eutrophication (Scheuerell and Schindler 2004). In all of the above examples, development of lake shorelines disrupted assemblages, habitat, or processes. Two special symposia on urbanization, one published in J-NABS (Walsh et al. 2005) and another published as a symposium proceedings of the American Fisheries Society (Brown et al. 2005) have made significant contributions to this body of work.

Landscape perspectives on coastal environments

Coastal areas can contain many hydrogeomorphic formations and ecosystem types, such as cliffs, beaches, and fringing, palustrine, and riverine wetlands (Keough et al. 1999). Thus, coastal areas provide a different set of challenges for linking landscape and response variables. A multiscale assessment designed to disentangle the influence of a human disturbance gradient and nonstressor covariables was implemented for coastal wetlands along the US side of the Laurentian Great Lakes (Danz et al. 2005, 2007, Brazner et al. 2007a, b). Distribution patterns among birds, amphibians, fish, invertebrates, wetland vegetation, and diatoms were better explained by geographic (lake) factors than by geomorphic factors (wetland type) or human disturbance (Brazner et al. 2007a). Environmental variables in this assessment were summarized across a range of spatial scales (100, 500, 1000, 5000 m, and whole catchments). Nonstressor covariables explained more variation in biological response variables than did disturbance variables, and responses were not concordant across assemblages (Brazner et al. 2007a). A human disturbance gradient also has been used to identify reference reaches in streams (e.g., Danz et al. 2005, 2007, Host et al. 2005, Wang et al. 2008).

An increase in landscape-related assessment papers in 2005 and 2006 corresponded with NABS-sponsored symposia at 2 annual meetings (“Determining and Using Reference Condition in Biological Assessments” and “Environmental Assessment Meets Landscape Ecology Meets Landuse Planning” (the latter summarized by Johnson et al. 2007). Many papers published during this period emphasized land use, particularly urbanization and agriculture, as factors regulating biological and chemical responses. An increase in assessment articles coincided with an increase in articles on ecological indicators of ecosystem health that incorporated landscape-scale influences (e.g., 2007 special issue of Journal of Great Lakes Research on coastal indicators). See Hawkins et al. (2010) and Dolédec and Statzner (2010) for discussions of reference approaches and bioassessment.

Landscape perspectives on assessing ecosystem functions and services

J-NABS has been a leader in publication of studies focused on measures of ecosystem function in aquatic ecosystems, and some of these studies have included a landscape component. The link between landscape characteristics and ecosystem functions received relatively little attention until recently, when efforts to understand the mechanisms by which human activities degrade aquatic ecosystems began to be focused on developing measures of ecosystem functions for use in environmental assessment programs.

Many measures of ecosystem function are not well suited for implementation over large regions because of the time and cost of field sampling or sample processing. However, altered ecosystem function is a symptom of human disturbance (Bunn and Davies 2000, Meyer et al. 2005, Sweeney et al. 2006) and should be included in evaluations of restoration success (Lake et al. 2007). Bunn et al. (1999) were among the first to implement a regional characterization of benthic gross primary production and respiration measures to assess the impact of catchment and riparian disturbance on stream ecosystems in Australia. A special issue of J-NABS, “Source-Water Monitoring: Combining Basic and Applied Research,” featured a comprehensive study of the New York City water-supply catchments. Sweeney et al. (2006) used a broad range of biological and ecosystem function measures, including (in part) macroinvertebrate assemblages (Kratzer et al. 2006), organic matter transport (Kaplan et al. 2006), primary production and ecosystem metabolism (Bott et al. 2006a, b), and nutrient and organic C uptake (Newbold et al. 2006) to assess the sources of the New York City drinking-water supply. They integrated these measures in a comprehensive analysis of local and landscape features that included variance partitioning across spatial scales (Arscott et al. 2006).

Net ecosystem production measures appear to be strongly controlled by local-scale factors, and are particularly sensitive to riparian cover (Bunn et al. 1999, Young and Huryn 1999, Bunn and Davies 2000, Bott et al. 2006b). Bott et al. (2006a) found negative correlations between gross primary production (GPP) and indicators of urbanization. In contrast, ecosystem metabolism was not correlated with indicators of urban land use in streams in the vicinity of Atlanta, Georgia (Meyer et al. 2005), although lower uptake velocity values for NH₄ and soluble reactive P were found in urban than in forested streams. Increased nutrient removal but reduced efficiency were associated with elevated nutrient concentrations, a pattern consistent with results of Mulholland et al. (2008) from streams across a wide range of land uses and geographic regions.

Runoff from agricultural fields and riparian vegetation alteration are associated with elevated total N and NO₃-N concentrations in nearby water bodies. The proportion of row-crop land use in the catchment can predict up to 86% of the total variation in N content (e.g., Johnson et al. 1997, Jones et al. 2001, Meador et al. 2005, Dodds and Oakes 2007). Such loadings are expected to alter N cycling. Denitrification rates did not differ among agricultural and urban streams in Indiana, USA, but NO₃-N concentrations were higher in the agricultural streams (Arango and Tank 2008). Mean annual nitrification and denitrification rates were positively correlated with % agriculture in the catchment and in 100-m buffers. Nitrification rates were best predicted by sediment C content (Arango and Tank 2008). Instream structures, such as debris dams and organic-rich gravel bars, were sites of intensive denitrification activities in suburban and urban streams (Groffman et al. 2005). Unfortunately, structures, such as debris dams, are among the first habitats removed by well-meaning landowners concerned about potential flood impacts. In addition, increased stream power in channelized urban streams can preclude the persistence of such structures. Increased nutrient loading also directly affects invertebrate consumers. Lower periphyton C:N and C:P ratios were associated with elevated nutrient (NH₄⁺ and soluble reactive P) excretion by mayflies (James et al. 2007), but body content nutrient concentrations and ratios did not differ across a range of agricultural landuse intensities.

The emerging emphasis on ecosystem functions and services probably will result in increased use of landscape ecology techniques for assessing environmental impacts and the success of restoration projects or ecosystem recovery. McTammany et al. (2007, 2008) examined ecosystem recovery from agricultural activity, and found that nutrient levels and suspended particles were higher in recovering streams than in forested streams. In recovering streams with well-developed riparian zones, GPP (McTammany et al. 2007) and stream temperatures (McTammany et al. 2008) were similar to those of forested streams, but the elevated nutrient concentrations indicated that the streams in which agricultural activities had ceased for ≥50 y had not yet fully recovered. Other ecosystem properties also have long recovery times. For example, abundance of large wood in Pacific Northwest streams is predicted to require ≥100 y following harvest in the riparian zones (e.g., Murphy and Koski 1989). Few studies have fully quantified recovery of ecosystem functions following a persistent disturbance, such as change in land use.