The Landscape Ecology of Rivers: from Patch-Based to Spatial Network Analyses
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Purpose of Review
We synthesize recent methodological and conceptual advances in the field of riverscape ecology, emphasizing areas of synergy with current research in landscape ecology.
Recent advances in riverscape ecology highlight the need for spatially explicit examinations of how network structure influences ecological pattern and process, instead of the simple linear (upstream-downstream) view. Developments in GIS, remote sensing, and computer technologies already offer powerful tools for the application of patch- and gradient-based models for characterizing abiotic and biotic heterogeneity across a range of spatial and temporal scales. Along with graph-based analyses and spatial statistical stream network models (i.e., geostatistical modelling), these approaches offer improved capabilities for quantifying spatial and temporal heterogeneity and connectivity relationships, thereby allowing for rigorous and high-resolution analyses of pattern, process, and scale relationships.
Spatially explicit network approaches are able to quantify and predict biogeochemical, hydromorphological, and ecological patterns and processes more precisely than models based on longitudinal or lateral riverine gradients alone. Currently, local habitat characteristics appear to be more important than spatial effects in determining population and community dynamics, but this conclusion may change with direct quantification of the movement of materials, energy, and organisms along channels and across ecosystem boundaries—a key to improving riverscape ecology. Coupling spatially explicit riverscape models with optimization approaches will improve land protection and water management efforts, and help to resolve the land sharing vs. land sparing debate.
KeywordsPatch-based models Gradient models Spatial statistical network models Network analyses Stream networks Riverscapes
Landscape ecology aims to elucidate the effects of large-scale variation in the structure and composition of habitat on ecological patterns and processes across a range of spatiotemporal scales [1, 2]. The discipline of landscape ecology originated in terrestrial systems and, traditionally, aquatic ecosystems were considered components of the larger landscape mosaic, or as subunits linked to the terrestrial landscape by cross-ecosystem flows of material and energy . However, it was soon recognized that landscape ecology questions are relevant within aquatic systems as well [3, 4, 5]. Differences in the environmental characteristics of terrestrial vs. aquatic systems may necessitate the use of different metrics and methods for quantifying the landscape mosaic. However, the overarching goal of quantifying effects of large-scale environmental heterogeneity on ecological patterns and processes—the cornerstone of landscape ecology as a discipline—is applicable across realms. Furthermore, there can be no doubt that efforts to unify landscape ecology research across terrestrial-aquatic boundaries will advance both the conceptual and methodological tools of the discipline [3, 6].
Q1: The Heterogeneity of Riverscapes
Riverscapes as Networks
From a human perspective, riverscapes are linear dendritic or anabranching systems; the spatial configuration and structure of which differ markedly from most terrestrial systems and other aquatic systems (i.e., lakes, ponds, and marine environments). The hierarchical branching structure of river networks has long been recognized, but most research in these systems occurred within linear branches of the network (i.e., individual stream channels), ignoring spatial relationships among branches within the larger network, connectivity among and within branches, and flow-mediated directionality of the network (non-network analysis, sensu Peterson et al. [8••]). Even the River Continuum Concept (hereafter RCC, Vannote et al. ), perhaps the most influential concept in stream ecology in the twentieth century, did not consider river systems as networks but instead predicted patterns and processes along linear channels spanning the longitudinal profile of systems, from headwaters to mouth. Recent conceptual advances, in contrast, emphasize the need for spatially explicit examinations of ecological processes in river networks, with particular emphasis on how network structure influences ecological patterns and processes [10, 11, 12, 13, 14•]. Along with this recognition come novel approaches for quantifying the ecological effects of network structure and habitat quality, configuration, and connectivity, which we outline below.
Patch vs. Gradient Models of Riverscapes
Two fundamental approaches exist for quantifying and modelling landscape structure and its effects on species and communities: patch-based and gradient models . Patch-based models, as the name implies, treat the landscape as a set of habitat patches representing environmentally homogenous subunits of the landscape. Landscape structure thus emerges from the composition, configuration, and connectivity relationships of patches with different sizes and qualities [15, 16]. In contrast, gradient models represent landscape structure based on continuous raster or grid data, without a priori delineation of patches or subunits. Here, grid cells or pixels are the smallest homogenous and discrete spatial units, allowing for a quasi-continuous change of characteristics across the landscape [15, 17].
Both patch-based and gradient models have been successfully applied to river networks. For example, as an alternative to the RCC view that biotic communities are controlled predominantly by continuous, longitudinal gradients in physical conditions, patch-based models have shown that discontinuous hierarchies of hydrogeomorphic patches can strongly influence on the spatial structure and composition of communities [18, 19, 20]. The spatial arrangement and temporal dynamics of these hydrogeomorphic patches (or “functional process zones,” sensu ) provide a useful template for the delineation of local communities in the river network, and a spatially explicit framework for environmental management [13, 21, 22, 23].
Recent developments in GIS, remote sensing technologies, and computer power offer powerful tools for the application of gradient models, too [24, 25]. For example, Scown et al.  applied airborne laser scanning (LiDAR) technology to measure spatial patterns in riverscape topography using moving window analyses of eight surface metrics at a resolution of 1 m2. The increasing availability of unmanned aerial vehicles (UAVs) has enabled the production of high-quality topographic data with a spatial resolution up to 10 cm and a vertical error to 50 cm [25, 45]. Even very basic UAVs can yield data on the complexity of riverscapes—including geomorphological, hydraulic, and ecological attributes—far beyond traditional field methods in resolution, accuracy, and efficiency . With these rapidly developing technologies, not only single reaches (i.e., ≈ 100 m of channel length), but also the entire segments, subcatchments, and even whole catchments can be routinely surveyed in a standardized manner . Data obtained from these spatial surveys will enable a better understanding of biogeomorphological patterns and processes at a wide range of scales, and may lead to more effective quantification of the links between hydromorphological conditions and ecological status of the riverscape [28, 29].
Similar to terrestrial systems, the decision to use patch-based or gradient models depends on study goals and the underlying structure of environmental heterogeneity [6, 15, 30, 31]. Generally, patch-based models are useful when both intra-patch environmental homogeneity and inter-patch environmental heterogeneity are high and observable. When these criteria are not met, gradient models are preferred. Due to the small size of sampling units (i.e., grids or cells), gradient models can be applied across spatial scales using a range of continuous variables and moving window analyses to examine the effect of scale on ecological pattern and process . However, results from gradient models may be difficult to apply to management when the continuous response variables do not have intuitive interpretations, or when the spatial resolution of model output does not match the scale of management ( [15, 31] but see, e.g., Baranya et al. ). Nevertheless, the accuracy and availability of technology for continuous environmental measurements are increasing rapidly, suggesting that gradient models will remain at the forefront of riverscape analyses [25, 32].
Graph analyses may be a useful modelling approach for riverscape ecology that overcomes the limitations of patch-based and gradient models while enhancing the interpretability and applicability of results [33, 34]. Briefly, graphs are a set of nodes and links. In landscape ecological applications, nodes can represent a particular environmental feature of habitat patches, a focal species, or an assemblage of species, whereas links represent the functional connections among nodes, such as the strength of interactions among species, the flow of energy among patches, or the dispersal of individuals among populations or communities [35, 36, 37]. Graphs can be depicted using patch-based, grid, or raster data. Using this modelling template, researchers can then apply graph-based indices to characterize ecological patterns and processes in a spatially explicit manner with simple, straightforward extensions to management. Despite relatively long-standing applications in terrestrial landscape ecology, graph analyses have only recently been applied in riverscape ecology (see, e.g., [13, 38, 39, 40). We return to this modelling tool below, in discussing methods for quantifying riverscape connectivity.
Connectivity is a vital element of natural landscapes, regulating the flow of genes, species, materials, and energy. However, human alteration of habitat often disrupts these flows, reducing connectivity and threatening biodiversity and ecosystem services worldwide [41, 42, 43]. River networks are especially susceptible to human-induced fragmentation effects due to their dendritic, linear structure [44, 45]. In fact, these connectivity relationships may be the most fundamental difference between riverscapes and terrestrial landscapes because the linear structure of rivers allows for disproportionately large effects of barriers. Studies show, for example, that hydropower dams can cause large—even continental scale—degradation of biodiversity and ecosystem services by reducing connectivity in freshwater networks [46, 47, 48]. Rarely could a single obstacle cause such extensive harm in terrestrial habitats, where alternative paths are often available to circumvent barriers.
Stemming largely from these connectivity relationships, the application of graph analysis methods in riverscape research has yielded significant advances in ecological understanding and management of freshwater systems in the last decade [13, 49]. With these methods, the effects of localized barriers can be quantified at the subcatchment or whole-network scale. For example, graph-based habitat availability indices were used to prioritize the removal of barriers in the catchment of the Tagus River, Portugal, to improve structural and functional connectivity [50, 51]. Similarly, graphs were used to quantify fragmentation effects on river networks in the Great Plains region of the USA and to select regions where barrier mitigation or flow restoration would be most beneficial for maintaining or restoring fish biodiversity .
Adding Temporal Dynamics to Riverscape Ecology
Historical analysis of riverscapes is crucial to understanding how channel-floodplain habitats evolved over time, which provides key benchmarks for management. Although effects of hydromorphological turnover on the freshwater biodiversity have long been recognized (e.g., [18, 19, 55]), recent analyses have quantified the spatio-temporal development of riverscapes with unprecedented resolution (e.g., [56, 57]). In particular, these studies show the key effects of interactions with riparian and in-channel vegetation on hydromorphological dynamics [58, 59]. This capacity of macrophyte vegetation to modify the physical environment has important implications for landform evolution and riparian biodiversity . Further, in a series of excellent studies, Bishop-Taylor et al. [39, 60, 61] showed that remotely sensed time series data, both hydrologic and physical, can be combined with graph and circuit theory to evaluate changes in habitat availability for aquatic organisms over space and time across a complex riverscape. Their studies also show the advantages of combining dynamic, remotely sensed time series data with static landscape connectivity maps to characterize the structural and functional connectivity of the riverscape . Such modelling approaches are needed to understand the consequences of large-scale drying and flooding on riverscape dynamics and connectivity patterns—a crucial step in predicting the effects of climate change on freshwater systems.
In addition to reconstructing the past, there is a pressing need to model and predict the future effects of climate change on riverscape ecology . Studies predict, for example, significant shifts in the distribution of fishes with increasing water temperature and changes in precipitation. Generally, cold-water fishes are predicted to experience range reductions due to upstream shifts in habitat, whereas the ranges of warm-water fishes will likely expand [63, 64]. However, these climate-related effects will undoubtedly interact with the topology of river networks and other human perturbations (e.g., urbanization, reservoir construction), underscoring the importance of riverscape-scale predictive modelling [65, 66]. Specifically, how species distributions and interactions (i.e., metacommunity dynamics) respond to climate change will almost certainly depend on the context of these processes within the larger river network.
Q2: Pattern, Process, and Scale Relationships
Spatial Statistical Network Models
One of the most important conceptual advances in understanding pattern, process, and scale relationships in riverscapes is the consideration of river networks as macrosystems. According to this concept, differences in spatial heterogeneity, connectivity, and asynchrony among elements of the network regulate ecological dynamics of the whole network, influencing system sensitivity, resistance, and resilience [67••]. Testing this concept requires spatially explicit, network-level statistical analyses developed recently by geostatistical modelers, and which offer valuable new insight on the scale dependence of hydrological and ecological patterns [8••, 68].
These novel geostatistical modelling approaches account for specific properties of stream networks in analyzing the spatial structure of data (e.g., stream biochemistry, species density), including branching structure, directed flow, longitudinal connectivity, and abrupt changes at tributary confluences [8••, 68]. Models can account for two types of spatial relationships among data points based on hydrologic (i.e., non-Euclidean) distance: flow connected and flow unconnected. The flow-connected (or tail up) spatial autocorrelation structure may be useful for modelling downstream flows of material and energy, whereas the flow-unconnected (or tail down) model allows for spatial autocorrelation between both flow-connected and flow-unconnected sampling locations (e.g., points along a single tributary vs. points in adjacent, independent tributaries). The flow-unconnected spatial autocorrelation structure may be useful for modelling the abundance of organisms which can actively move both upstream and downstream (e.g., fish, amphibians, macroinvertebrates). For example, McGuire et al.  used 664 water samples collected every 100 m throughout 32 tributaries in a fifth-order river network to quantify spatial patterns of chemical constituents over a range of scales using empirical semivariograms that explicitly incorporated network topology. By examining the spatial dependence of the data, it was possible to separate the effects of fine- vs. broad-scale processes and in-stream vs. landscape processes on chemical variables. In another study, Filipe et al.  used these geostatistical analyses to better understand the distribution of invasive crayfish species in river networks.
In sum, these spatial statistical network models represent significant advances for quantifying and understanding the effects of network structure on patterns and processes in riverscapes. At present, however, this modelling framework operates with single response variables because the models are extensions of a basic linear model in which an autocovariance function is added to quantify the covariance between any two data points as a function of spatial distance. There is a great need for a multivariate analogue—similar models that accommodate multivariate response variables (e.g., community composition, multi-species abundance data, genotypes). Existing spatial statistical methods for multivariate response variables, such as partitioning variance between spatial and environmental fractions in redundancy analysis, have limited capacity to accommodate network properties, in addition to other weaknesses [71, 72, 73]. It would be especially useful if both univariate and multivariate response variables could be analyzed using the same modelling framework to ensure that differences in analytical methodology do not lead to differences in the interpretation of the results.
Effect of Network Structure on Niche vs. Spatial Processes
Despite the many recent conceptual and methodological advances in riverscape ecology, understanding how network structure mediates effects of environmental (i.e., niche based) vs. dispersal processes on biodiversity remains a central challenge. In an influential study, Brown and Swan  tested whether the influence of niche vs. dispersal processes changed depending on the position of habitat in the river network. Their results suggested that low-order, headwater streams are environmentally diverse and largely isolated from downstream elements of the network. As a consequence, local environmental conditions and species interactions should be the primary determinants of community structure in upstream areas, whereas the influence of dispersal should increase moving downstream (i.e., into higher-order streams), where habitats are in a more central position in the river network [74, 75]. Other theoretical studies predict strong effects of network structure on niche-based vs. dispersal processes, and on associated population or community dynamics [76, 77, 78], but empirical research has, thus far, found only weak support for the network position hypothesis (NPH; Schmera et al. ). In one of the most detailed, large-scale field studies using graph-based indices to examine the role of network position on community organization, Henriques-Silva et al.  found that (1) the NPH was not supported in catchments with high heterogeneity in connectivity among sites, and that (2) in more homogeneously connected catchments, the NPH was only supported when headwaters were more environmentally heterogeneous than downstream sites. Overall, these results suggest that environmental control, stochasticity, and the effects of human alteration override clear topological effects on communities in real riverscapes (see also ).
One of the most intriguing questions remaining in riverscape research is how ecological patterns and processes are affected by connectivity along the upstream-downstream gradient [74, 79, 82], or by lateral hydrologic connectivity in large alluvial rivers [83, 84]. A growing number of studies suggest that local characteristics of the habitat (environmental heterogeneity, hydromorphology, etc.) are more important than spatial effects in determining population and community dynamics in riverscapes [13, 85]. However, several studies also suggest that spatial dynamics can modify the effects of these local characteristics [86, 87], whether acting within stream channels, by overland pathways between channels, or between terrestrial and aquatic ecosystems [6, 81]. Recent conceptual papers thus emphasize the value of more mechanistic, spatially explicit approaches to understanding spatial population and community dynamics in riverscapes [88••, 89]. Specifically, these studies call for direct quantification of the movement of materials, energy, and organisms (e.g., dispersal, foraging, migration) along channels and across ecosystem boundaries. This “metaecosystem” view integrates the full spectrum of spatial connections among landscape elements, combining the core concepts of landscape and ecosystem ecology into a unified framework for spatial ecology research [88••]. Measuring such a diversity of flows across a range of spatial and temporal scales is an extremely challenging task, but there is no doubt that these approaches offer unprecedented resolution of the structure and function of riverscapes . Importantly, the use of direct data on the movement of materials, energy, and organisms guards against overreliance on indirect inference of spatial processes—a common problem in both landscape and riverscape research .
Q3: Management Issues
Understanding how environmental heterogeneity affects ecological patterns, processes, and scale relationships is necessary for scientifically sound management. Consequently, a key strength of landscape ecology has been its contributions to land use planning and land protection. In this regard, land sharing vs. land sparing debate is a pressing issue where scientific evaluation would be of great value. Land sparing refers to the protection of some land while the rest is used for agricultural production (or other ecosystem services), whereas land sharing requires the full protection of less land, but the use of more biodiversity-friendly strategies in agricultural landscapes [91, 92]. We are only beginning to understand the trade-offs associated with these two strategies, but it is already clear that choices between land sharing vs. land sparing strategies not only affect terrestrial systems, but also the ecological status of riverscapes . While it is also clear that conservation decisions for terrestrial systems cannot be separated from those related to the protection of river networks and riparian zones, integrated conservation planning for riverscapes has lagged, and conservation planning is typically conducted separately for terrestrial and freshwater systems [94, 95].
The catchments of streams and rivers encompass both terrestrial and aquatic landscapes, and functional catchments maintain the natural flows of elements, material, energy, and organisms between the two realms [96, 97]. Therefore, it is logical that catchments should be the basic units of conservation management, rather than basing management on terrestrial landscape elements or jurisdictional boundaries . Fortunately, recent management frameworks recognize the need for spatially informed and strategic approaches that protect biodiversity and ecosystem services at the catchment scale [99, 100, 101•, 102]. For example, Erős et al. [101•] propose a unified model for riverscape conservation that systematically guides management actions to protect both terrestrial and aquatic biodiversity and ecosystem services. This framework suggests applying biodiversity and ecosystem service indicators to prioritize land use within and among catchments, and an optimization-based approach for identifying the most suitable catchments for protection that incorporates connectivity restoration. This approach is thus based on a combination of land sparing and land sharing strategies to achieve sustainable water resources.
With climate change, freshwater management will continue to be a critical issue for humankind, and this “battle for water” will likely accelerate the deterioration of riverscapes. Sustainable management of riverscapes will thus necessitate not only intensive negotiations among stakeholder groups, but also creative uses of socio-economic and socio-ecological data to find compromise in the land sparing vs. land sharing debate [103, 104••]. It is our hope that riverscape models will continue to evolve to accommodate these cross-disciplinary data while remaining transparent enough to allow stakeholders from diverse backgrounds to understand and apply to pressing water management questions.
In the last two decades, concepts and methods from landscape ecology have been adapted for application to river networks and integrated terrestrial-aquatic systems. These advances have, in turn, dramatically increased our understanding of how large-scale variation in the structure and composition of habitat affects ecological patterns and processes throughout riverscapes. This review highlights what we see as particularly important recent improvements in quantifying spatial and temporal heterogeneity of riverscapes, and shows that both patch-based and gradient models are useful for quantifying environmental heterogeneity and biodiversity-environment relationships. The suitability and applicability of these models depend on study purpose, the researcher’s ability to observe and quantify environmental heterogeneity at relevant scales, and data availability. Nevertheless, with continued improvements in the accuracy, resolution, and availability of remote sensing technology, we believe that applications of gradient-based models to riverscape ecology will likely expand in numbers and power. Recent studies also show the versatility of spatial graph and network models for understanding and interpreting scale-dependent patterns and processes in riverscapes. Specifically, spatially explicit network approaches are able to quantify and predict biogeochemical, hydromorphological, and ecological patterns and processes more precisely than models based on longitudinal or lateral riverine gradients alone. Finally, we see great value in coupling spatially explicit riverscape models with optimization approaches to guide land protection, water management, and to resolve the land sharing vs. land sparing debate.
Despite these great strides in modelling and applications, there remain many unanswered questions related to ecological structure and function in riverscapes. One striking example comes from recent satellite imagery and modelling analyses suggesting that the length and surface area of river networks is much higher than previously thought . Among many important implications, these findings suggest that riverscapes play a greater role in controlling land-atmosphere carbon fluxes than is currently represented in global budgets, and call for explicit integration of riverscape and climate models. More broadly, this study makes clear the need for more innovative technological, analytical, and modelling tools to understand and protect ecological processes occurring within riverscapes, as well as those involving interactions with terrestrial and atmospheric systems.
TE conceived the main ideas of the study and led writing of the manuscript with the contribution of WL. Both TE and WL contributed to editing manuscript drafts and gave final approval for publication.
Open access funding provided by MTA Centre for Ecological Research (MTA ÖK). The work of TE was supported by the GINOP 2.3.3-15-2016-00019 and the Ecology for Society (MTA KEP) project.
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Conflict of Interest
The authors have no conflict of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
- 1.Turner MG, Gardner RH. Introduction to landscape ecology and scale. In: Landscape Ecology in Theory and Practice. Springer, New York, NY, USA; 2015. p. 1–32.Google Scholar
- 2.Wu JG. Key concepts and research topics in landscape ecology revisited: 30 years after the Allerton Park workshop. Landsc Ecol. 2013;28:1–11.Google Scholar
- 3.Wiens JA. Riverine landscapes: taking landscape ecology into the water. Freshw Biol. 2002;47:501–15.Google Scholar
- 4.Allan JD. Landscapes and riverscapes: the influence of land use on stream ecosystems. Annu Rev Ecol Evol Syst. 2004;35:257–84.Google Scholar
- 5.Wu J. Seascape ecology and landscape ecology: distinct, related, and synergistic. In: Simon J. Pittman (editor), Seascape Ecology, Wiley-Blackwell. 2018; p. 487–491.Google Scholar
- 6.Erős T, Campbell-Grant EH. Unifying research on the fragmentation of terrestrial and aquatic habitats: patches, connectivity and the matrix in riverscapes. Freshw Biol. 2015;60:1487–501.Google Scholar
- 7.Fausch KD, Torgersen CE, Baxter CV, Li HW. Landscapes to riverscapes: bridging the gap between research and conservation of stream fishes. Bioscience. 2002;52:483–98.Google Scholar
- 8.•• Peterson EE, Ver Hoef JM, Isaak DJ, Falke JA, Fortin MJ, Jordan CE, et al. Modelling dendritic ecological networks in space: an integrated network perspective. Ecol Lett. 2013;16:707–19 This study provides an overview of spatial statistical network models for dendritic stream networks.PubMedGoogle Scholar
- 9.Vannote RL, Minshall GW, Cummins KW, Sedell JR, Cushing CE. The river continuum concept. Can J Fish Aquat Sci. 1980;37:130–7.Google Scholar
- 10.Benda L, Poff NL, Miller D, Dunne T, Reeves G, Pess G, et al. The network dynamics hypothesis: how channel networks structure riverine habitats. Bioscience. 2004;54:413–27.Google Scholar
- 12.Altermatt F. Diversity in riverine metacommunities: a network perspective. Aquat Ecol. 2013;47:365–77.Google Scholar
- 13.Erős T. Scaling fish metacommunities in stream networks. Synthesis and future research avenues. Community Ecol. 2017;18:72–86.Google Scholar
- 14.• Tonkin JD, Altermatt F, Finn DS, Heino J, Olden JD, Pauls SU, et al. The role of dispersal in river network metacommunities: patterns, processes, and pathways. Freshw Biol. 2018;63:141–63 An important synthesis of current knowledge on the role of dispersal in stream metacommunities.Google Scholar
- 15.Lausch A, Blaschke T, Haase D, Herzog F, Syrbe R-U, Tischendorf L, et al. Understanding and quantifying landscape structure – a review on relevant process characteristics, data models and landscape metrics. Ecol Model. 2015;295:31–41.Google Scholar
- 16.Turner MG. Landscape ecology: the effect of pattern on process. Annu Rev Ecol Syst. 1989;20:171–97.Google Scholar
- 17.McGarigal K, Tagil S, Cushman SA. Surface metric: an alternative to patch metrics for the quantification of landscape structure. Landsc Ecol. 2009;24:433–50.Google Scholar
- 18.Poole GC. Fluvial landscape ecology: addressing uniqueness within the river discontinuum. Freshw Biol. 2002;47:641–60.Google Scholar
- 19.Thorp JH, Thoms MC, Delong MD. The riverine ecosystem synthesis: biocomplexity in river networks across space and time. River Res Appl. 2006;22:123–47.Google Scholar
- 20.Thorp JH. Metamorphosis in river ecology: from reaches to macrosystems. Freshw Biol. 2014;59:200–10.Google Scholar
- 21.Thorp JH, Flotemersch JE, Delong MD, Casper AF, Thoms MC, Ballantyne F, et al. Linking ecosystem services, rehabilitation, and river hydrogeomorphology. Bioscience. 2010;60:67–74.Google Scholar
- 23.Thoms M, Scown M, Flotemersch J. Characterization of river networks: A GIS approach and its applications. J Am Water Resour As. 2018;1-15.Google Scholar
- 25.Rusnák M, Sládek J, Kidová A, Lehotský M. Template for high-resolution river landscape mapping using UAV technology. Measurement. 2018;115:139–51.Google Scholar
- 26.Scown MW, Thoms MC, DeJager NR. Measuring floodplain spatial patterns using continuous surface metrics at multiple scales. Geomorphology. 2015;245:87-101.Google Scholar
- 27.Cook KL. An evaluation of the effectiveness of low-cost UAVsand structure from motion for geomorphic change detection. Geomorphology. 2017;278:195–208.Google Scholar
- 28.Bizzi S, Demarchi L, Grabowski RC, Weissteiner CJ, Van de Bund W. The use of remote sensing to characterise hydromorphological properties of European rivers. Aquat Sci. 2016;78:57–70.Google Scholar
- 29.Belletti B, Rinaldi M, Bussettini M, Comiti F, Gurnell AM, Mao L, et al. Characterising physical habitats and fluvial hydromorphology: a new system for the survey and classification of river geomorphic units. Geomorphology. 2017;283:143–57.Google Scholar
- 30.Erős T, Olden JD, Schick RS, Schmera D, Fortin M. Characterizing connectivity relationships in freshwaters using patch-based graphs. Landsc Ecol. 2012;27:303–17.Google Scholar
- 31.Frazier AE, Kedron P. Landscape metrics: past progress and future directions. Curr Landscape Ecol Rep. 2017;2:63–72.Google Scholar
- 32.Baranya S, Fleit G, Józsa J, Szalóky Z, Tóth B, Erős T. Habitat mapping of riverine fish by means of hydromorphological tools. Ecohydrology. 2018;11:e2009.Google Scholar
- 34.Dilts TE, Weisberg PJ, Leitner P, Matocq MD, Inman RD, Nussear KE, et al. Multiscale connectivity and graph theory highlight critical areas for conservation under climate change. Ecol Appl. 2016;26:1223–37.Google Scholar
- 35.Fall A, Fortin M-J, Manseau M, O’Brien D. Spatial graphs: principles and applications for habitat connectivity. Ecosystems. 2007;10:448–61.Google Scholar
- 36.Dale MRT, Fortin M-J. From graphs to spatial graphs. Annu Rev Ecol Evol Syst. 2010;41:21–38.Google Scholar
- 37.Galpern P, Manseau M, Fall A. Patch-based graphs of landscape connectivity: a guide to construction, analysis and application for conservation. Biol Conserv. 2011;144:44–55.Google Scholar
- 38.Fullerton AH, Anzalone S, Moran P, Van Doornik DM, Copeland T, Zabel RW. Setting spatial conservation priorities despite incomplete data for characterizing metapopulations. Ecol Appl. 2016;26:2560–80.Google Scholar
- 40.Neufeld K, Watkinson DA, Tierney K, Poesch MS. Incorporating asymmetric movement costs into measures of habitat connectivity to assess impacts of hydrologic alteration to stream fishes. Divers Distrib. 2018;24:593–604.Google Scholar
- 41.Fahrig L. Effects of habitat fragmentation on biodiversity. Annu Rev Ecol Evol S. 2003;34:487–515.Google Scholar
- 43.Fisher J, Lindenmayer DB. Landscape modification and habitat fragmentation: a synthesis. Glob Ecol Biogeogr. 2007;16:265–80.Google Scholar
- 44.Fagan WF. Connectivity, fragmentation, and extinction risk in dendritic metapopulations. Ecology. 2002;83:3243–9.Google Scholar
- 45.Crook DA, Lowe WH, Allendorf FW, Erős T, Finn DS, Gillanders BM, Hadwen WL, Harrod C, Hermoso V, Jennings S, Kilada RW, Nagelkerken I, Hansen MM, Page TJ, Riginos C, Fry B, Hughes JM. Human effects on ecological connectivity in aquatic ecosystems. Integrating scientific approaches to support management and mitigation. Sci Total Environ. 2015;534:52–64.PubMedGoogle Scholar
- 48.Ziv G, Baran E, Nam S, Rodríguez-Iturbe I, Levin SA. Trading-off fish biodiversity, food security, and hydropower in the Mekong River Basin. P Natl A Sci. 2012;109:5609–14.Google Scholar
- 49.Erős T, Schmera D, Schick RS. Network thinking in riverscape conservation – a graph-based approach. Biol Conserv. 2011;144:184–92.Google Scholar
- 50.Segurado P, Branco T, Ferreira MT. Prioritizing restoration of the structural connectivity of rivers: a graph-based approach. Landsc Ecol. 2013;28:1231–8.Google Scholar
- 51.Branco P, Segurado P, Santos JM, Ferreira MT. Prioritizing barrier removal to improve functional connectivity of rivers. J Appl Ecol. 2014;51:1197–206.Google Scholar
- 52.Perkin JS, Gido KB, Cooper AR, Turner TF, Osborne MJ, Johnson ER, et al. Fragmentation and dewatering transform Great Plains stream fish communities. Ecol Monogr. 2015;85:73–92.Google Scholar
- 53.Chaput-Bardy A, Alcala N, Secondi J, Vuilleumier S. Network analysis for species management in river networks: application to the Loire River. Biol Conserv. 2017;210:26–36.Google Scholar
- 54.Lehotský M, Rusnák M, Kidová A, Dudžák J. Multitemporal assessment of coarse sediment connectivity along a braided-wandering river. Land Degrad Dev. 2018;29:1249–61.Google Scholar
- 55.Townsend CR. The patch dynamics concept of stream community ecology. J N Am Benthol Soc. 1989;8:36–50.Google Scholar
- 56.Hohensinner S, Jungwirth M, Muhar S, Schmutz S. Spatio-temporal habitat dynamics in a changing Danube River landscape 1812—2006. River Res Appl. 2011;27:939–55.Google Scholar
- 57.Díaz-Redondo M, Marchamalo M, Egger G, Magdaleno F. Toward floodplain rejuvenation in the middle Ebro River (Spain): from history to action. Geomorphology. 2018;317:117–27.Google Scholar
- 58.Francis RA, Corenblit D, Edwards PJ. Perspectives on biogeomorphology, ecosystem engineering and self-organization in island-braided fluvial ecosystems. Aquat Sci. 2009;71:290–304.Google Scholar
- 59.Gurnell AM, Rinaldi M, Belletti B, Bizzi S, Blamauer B, Braca G, et al. A multi-scale hierarchical framework for developing understanding of river behaviour to support river management. Aquat Sci. 2016;78:1–16.Google Scholar
- 60.Bishop-Taylor R, Tulbure MG, Broich M. Evaluating static and dynamic landscape connectivity modelling using a 25-year remote sensing time series. Landsc Ecol. 2018b;33:625–40.Google Scholar
- 61.Bishop-Taylor R, Tulbure MG, Broich M. Evaluating static and dynamic landscape connectivity modelling using a 25-year remote sensing time series. Landscape Ecol. 2018b;33:625-640.Google Scholar
- 62.Knouft JH, Ficklin DL. The potential impacts of climate change on biodiversity in flowing freshwaters systems. Annu Rev Ecol Evol S. 2017;48:111–33.Google Scholar
- 63.Buisson L, Thuiller W, Lek S, Lim P, Grenouillet G. Climate change hastens the turnover of stream fish assemblages. Glob Chang Biol. 2008;14:2232–48.Google Scholar
- 64.Comte L, Grenouillet G. Do stream fish track climate change? Assessing distribution shifts in recent decades. Ecography. 2013;36:1236–46.Google Scholar
- 67.•• McCluney KE, Poff NL, Palmer MA, Thorp JH, Poole GC, Williams BS, et al. Riverine macrosystems ecology: sensitivity, resistance, and resilience of whole river basins with human alterations. Front Ecol Environ. 2014;12:48–58 This study shows a useful template to view riverscapes from a macrosystem perspective. Google Scholar
- 68.Isaak DJ, Peterson EE, Ver Hoef JM, Wenger SJ, Falke JA, Torgersen CE, et al. Applications of spatial statistical network models to stream data. WIREs Water. 2014;1:277–94.Google Scholar
- 69.McGuire KJ, Torgersen CE, Likens GE, Buso DC, Lowe WH, Bailey SW. Network analysis reveals multiscale controls on streamwater chemistry. P Natl Acad Sci USA. 2014;111:7030–7035.Google Scholar
- 70.Filipe AF, Quaglietta L, Ferreira M, Magalhães MF, Beja P. Geostatistical distribution modelling of two invasive crayfish across dendritic stream networks. Biol Invasions. 2017;19:2899–912.Google Scholar
- 71.Gilbert B, Bennett JR. Partitioning variation in ecological communities: do the numbers add up? J Appl Ecol. 2010;47:1071–82.Google Scholar
- 72.Smith TW, Lundholm JT. Variation partitioning as a tool to distinguish between niche and neutral processes. Ecography. 2010;33:648–55.Google Scholar
- 73.Sály P, Erős T. Effect of field sampling design on variation partitioning in a dendritic stream network. Ecol Complex. 2017;28:187–99.Google Scholar
- 76.Labonne J, Ravigné V, Parisi B, Gaucherel C. Linking dendritic network structures to population demogenetics: the downside of connectivity. Oikos. 2008;117:1479–90.Google Scholar
- 77.Auerbach DA, Poff NL. Spatiotemporal controls of simulated metacommunity dynamics in dendritic networks. J N Am Benthol Soc. 2011;30:235–51.Google Scholar
- 78.Terui A, Ishiyama N, Urabe H, Ono S, Finlay JC, Nakamura F. (2018). Metapopulation stability in branching river networks. P Natl Acad Sci USA 2018;115:E5963-E5969.Google Scholar
- 79.Schmera D, Árva D, Boda P, Bódis E, Bolgovics Á, Borics G, et al. Does isolation influence the relative role of environmental and dispersal-related processes in stream networks? An empirical test of the network position hypothesis using multiple taxa. Freshw Biol. 2018;63:74–85.Google Scholar
- 80.Henriques-Silva R, Logez M, Reynaud N, Tedesco PA, Brosse S, Januchowski-Hartley SR, et al. A comprehensive examination of the network position hypothesis across multiple river metacommunities. Ecography. 2018;42:284–94.Google Scholar
- 81.Tonkin JD, Heino J, Sundermann A, Haase P, Jähnig SC. Context dependency in biodiversity patterns of central German stream metacommunities. Freshw Biol. 2016;61:607–20.Google Scholar
- 82.Lowe WH. Landscape-scale spatial population dynamics in human-impacted stream systems. Environ Manag. 2002;30:225–33.Google Scholar
- 83.Ward J, Tockner K, Uehlinger U, Malard F. Understanding natural patterns and processes in river corridors as the basis for effective river restoration. Regul Rivers: Res Mgmt. 2001;17:311–23.Google Scholar
- 85.Heino J, Melo AS, Siqueira T, Soininen J, Valanko S, Bini LM. Metacommunity organisation, spatial extent and dispersal in aquatic systems: patterns, processes and prospects. Freshw Biol. 2015;60:845–69.Google Scholar
- 86.Lowe WH. Linking dispersal to local population dynamics: a case study using a headwater salamander system. Ecology. 2003;84:2145–54.Google Scholar
- 87.Czeglédi I, Sály P, Takács P, Dolezsai A, Nagy SA, Erős T. The scales of variability of stream fish assemblages at tributary confluences. Aquat Sci. 2015;78:641–54.Google Scholar
- 88.•• Gounand I, Harvey E, Little CJ, Altermatt F. Meta-ecosystems 2.0: rooting the theory into the field. Trends Ecol Evol. 2018;33:36–46 An important contribution which emphasizes the better integration of landscape ecology and meta-ecosystem ecology into a single framework of spatial ecology.PubMedGoogle Scholar
- 92.Fisher J, Abson DJ, Butsic V, Chappell MJ, Ekroos J, Hanspach J, et al. Land sparing versus land sharing: moving forward. Conserv Lett. 2014;7:149–57.Google Scholar
- 93.Koning AA, Moore J, Suttidate N, Hannigan R, McIntyre PB. Aquatic ecosystem impacts of land sharing versus sparing: nutrient loading to Southeast Asian rivers. Ecosystems. 2017;20:393–405.Google Scholar
- 94.Abell R, Allan JD, Lehner B. Unlocking the potencial of protected areas for freshwaters. Biol Conserv. 2007;134:48–63.Google Scholar
- 95.Nel JL, Reyers B, Roux DJ, Cowling RM. Expanding protected areas beyond their terrestrial comfort zone: identifying spatial options for river conservation. Biol Conserv. 2009;142:1605–16.Google Scholar
- 96.Liken GE, Bormann FH. Linkages between terrestrial and aquatic ecosystems. Bioscience. 1974;24:447–56.Google Scholar
- 97.Lowe WH, Likens GE. Moving headwater streams to the head of the class. Bioscience. 2005;55:196–7.Google Scholar
- 98.Doody DG, Withers PJA, Dils RM, McDowell RW, Smith V, McElarney YR, et al. Optimizing land use for the delivery of catchment ecosystem services. Front Ecol Environ. 2016;14:325–32.Google Scholar
- 99.Terrado M, Momblanch A, Bardina M, Boithias L, Munné A, Sabater S, et al. Integrating ecosystem services in river basin management plans. J Appl Ecol. 2016;53:865–75.Google Scholar
- 100.Zheng H, Li Y, Robinson BE, Liu G, Ma D, Wang F, et al. Using ecosystem service trade-offs to inform water conservation policies and management practices. Front Ecol Environ. 2016;14:527–32.Google Scholar
- 101.• Erős T, O’Hanley JR, Czeglédi I. A unified model for optimizing riverscape conservation. J Appl Ecol. 2018;55:1871–83 This study presents a modelling framework to directly integrate and optimize river conservation, ecosystem services delivery, and connectivity restoration planning.Google Scholar
- 102.Hermoso V, Cattarino L, Linke S, Kennard MJ. Catchment zoning to enhance co-benefits and minimize trade-offs between ecosystem services and freshwater biodiversity conservation. Aquatic Conserv: Mar Freshw Ecosyst. 2018;28:1004–14.Google Scholar
- 103.Palomo I, Montes C, Martín-López B, González JA, García-Llorente M, Alcorlo P, et al. Incorporating the socio-ecological approach in protected areas in the Anthropocene. Bioscience. 2014;64:181–91.Google Scholar
- 104.•• Poff NL, Brown CM, Grantham TE, Matthews JH, Palmer MA, Spence CM, et al. Sustainable water management under future uncertainty with eco-engineering decision scaling. Nat Clim Chang. 2016;6:25 A decision framework that explicitly and quantitatively explores trade-offs in engineering and ecological performance metrics across a range of management actions under unknown future hydrological and climate states.Google Scholar
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