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Article

Separate Versus Unified Ecological Networks: Validating a Dual Framework for Biodiversity Conservation in Anthropogenically Disturbed Freshwater–Terrestrial Ecosystems

by
Tianyi Cai
1,†,
Qie Shi
1,2,†,
Tianle Luo
1,
Yuechun Zheng
1,
Xiaoming Shen
2,3 and
Yuting Xie
1,*
1
Institute of Landscape Architecture, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
2
Center for Balanced Architecture, Zhejiang University, Hangzhou 310058, China
3
The Architectural Design & Research Institute of Zhejiang University Co., Ltd., Zhejiang University, Hangzhou 310028, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Land 2025, 14(8), 1562; https://doi.org/10.3390/land14081562
Submission received: 20 June 2025 / Revised: 22 July 2025 / Accepted: 28 July 2025 / Published: 30 July 2025
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Abstract

Freshwater ecosystems—home to roughly 10% of known species—are losing biodiversity to river-morphology alteration, hydraulic infrastructure, and pollution, yet most ecological network (EN) studies focus on terrestrial systems and overlook hydrological connectivity under human disturbance. To address this, we devised and tested a dual EN framework in the Yangtze River Delta’s Ecological Green Integration Demonstration Zone, constructing freshwater and terrestrial networks independently before merging them. Using InVEST Habitat Quality, MSPA, the MCR model, and Linkage Mapper, we delineated sources and corridors: freshwater sources combined NDWI-InVEST indicators with a modified, sluice-weighted resistance surface, producing 78 patches (mean 348.7 ha) clustered around major lakes and 456.4 km of corridors (42.50% primary). Terrestrial sources used NDVI-InVEST with a conventional resistance surface, yielding 100 smaller patches (mean 121.6 ha) dispersed across woodlands and agricultural belts and 658.8 km of corridors (36.45% primary). Unified models typically favor large sources from dominant ecosystems while overlooking small, high-value patches in non-dominant systems, generating corridors that span both freshwater and terrestrial habitats and mismatch species migration patterns. Our dual framework better reflects species migration characteristics, accurately captures dispersal paths, and successfully integrates key agroforestry-complex patches that unified models miss, providing a practical tool for biodiversity protection in disturbed freshwater–terrestrial landscapes.

1. Introduction

Freshwater ecosystems harbor exceptional biodiversity, hosting approximately 10% of all known species while occupying less than 1% of Earth’s surface area [1,2,3]. However, these ecosystems are experiencing severe biodiversity loss primarily driven by urban expansion-induced river morphological alterations, hydraulic infrastructure development, and water pollution [4,5]. Notably, while terrestrial and freshwater systems face distinct primary stressors, many anthropogenic pressures—including urbanization, pollution, and land use changes—simultaneously affect both systems and their interfaces, creating significant interactions and overlaps in the drivers of biodiversity loss despite their different dominant processes [6,7]. However, freshwater ecosystems are particularly severely affected by such disturbances. This vulnerability is amplified by the intrinsic connectivity of freshwater systems, where disturbances propagate across entire watersheds. Unlike terrestrial ecosystems, which are primarily affected by localized habitat loss and fragmentation, freshwater ecosystems face both direct structural modifications (e.g., damming, river channelization) and indirect, large-scale pressures disturbances such as upstream pollution and flow alterations that disrupt downstream ecological processes [8,9,10]. This multi-scale vulnerability contributes to the more rapid biodiversity decline observed in freshwater compared to terrestrial systems, underscoring the urgency of freshwater ecosystem conservation [11].
In this context, connectivity—defined as the degree to which landscapes facilitate or impede movement among habitat patches—has emerged as a critical indicator for freshwater ecosystem biodiversity conservation [12]. Connectivity encompasses two complementary dimensions: structural connectivity, which quantifies the physical continuity and spatial arrangement of landscape elements without considering species-specific responses [13]. Baguette et al. (2013) emphasized the importance of landscape structure in facilitating organism movement across fragmented habitats, illustrating the role of structural connectivity [13]; and functional connectivity, which measures the actual behavioral response of organisms to landscape features and their success in moving between habitat patches [12,14]. Field & Parrott (2022) mapped the functional connectivity of multiple ecosystem services across a regional landscape, highlighting key areas where service flows are spatially connected and providing valuable information for landscape-scale planning [14]. In contemporary urban expansion processes, land use changes significantly impact structural connectivity, leading to river morphological alterations and terrestrial habitat reduction or fragmentation [15]. Simultaneously, other human activities—including hydraulic infrastructure construction, water quality degradation, and land pollution—limit the mobility of both aquatic and terrestrial species, substantially affecting the functional connectivity of freshwater and terrestrial ecosystems [16], thereby threatening biodiversity and normal ecological processes.
While these two dimensions are widely applied in terrestrial systems, freshwater ecosystems exhibit fundamentally different movement dynamics due to the directional and fluctuating nature of water flows. Therefore, connectivity assessment in freshwater ecosystems must additionally consider hydrological processes, giving rise to the concept of hydrological connectivity—a water-specific framework that is distinct from terrestrial connectivity approaches [17]. Hydrological connectivity refers to water-mediated transfers of matter, energy, or species within or between elements of the hydrological cycle [18,19]. It includes longitudinal (e.g., upstream to downstream), lateral (e.g., between river and floodplain), and vertical (e.g., between surface and groundwater) connectivity [20,21,22]. This multi-dimensional nature distinguishes hydrological connectivity from terrestrial connectivity approaches, where movement patterns are primarily driven by habitat patch distribution [18,20]. In contrast, freshwater systems involve water-mediated transport processes and hierarchical watershed networks, where connectivity operates through distinct directional flows and across multiple interconnected spatial scales [9,23]. Among these dimensions, longitudinal connectivity is fundamental, facilitating the transport of water, sediments, and species along the river continuum, and is thus essential for freshwater ecosystem conservation [20].
Over the past two decades, various methods—such as graph theory [19,23] and hydrological–hydraulic approaches [24,25]—have been developed to quantify longitudinal connectivity, primarily capturing its structural dimension [26]. However, structural connectivity alone is insufficient for understanding species dispersal, as it does not account for behavioral responses or ecological conditions that constrain actual movement [27,28]. For instance, river reaches that appear physically connected may still be functionally disconnected due to barriers such as degraded water quality, unsuitable habitats, or hydraulic infrastructure like sluices and pumping stations [27,29,30]. Functional connectivity assessments require detailed biological observations, which are difficult to obtain across large watersheds [31]. To address this challenge, recent approaches have introduced proxy-based indicators—such as habitat quality and ecosystem service provision—as substitutes for direct observation [32,33]. Pe’er et al. (2011) employed individual-based models, proposing to decompose ecological functional connectivity into multiple components (habitat quality, habitat area, habitat connectivity, and species dispersal ability) to more accurately assess the impacts of different factors on ecosystems [32]. Shen et al. (2023) improved ecological function by enhancing habitat quality and ecosystem service provision [33]. These studies demonstrate that proxy indicators of habitat quality and ecosystem service provision can effectively supplement traditional ecological functional observations, providing a more comprehensive framework for ecological health assessment [32,33]. When combined with structural connectivity metrics, these proxies enable more comprehensive and ecologically meaningful evaluations of hydrological connectivity in human-impacted freshwater systems.
To translate connectivity assessments into spatial conservation strategies, watershed-scale freshwater ecosystem planning has increasingly adopted ecological networks (ENs) as an effective approach for restoring and maintaining hydrological connectivity across watershed landscapes [34,35]. Although originally developed for terrestrial systems, ENs have recently gained attention in freshwater conservation for their potential to support hydrological connectivity planning at the watershed scale [19,26]. Typically, ENs comprise core ecological sources interconnected by ecological corridors, offering an explicit spatial configuration for conservation planning [36,37,38]. Building on this framework, EN construction has evolved methodologically over the past two decades, generally following the paradigm of “ecological source identification—resistance surface design—ecological corridor extraction” [39,40].
However, significant limitations remain when applying existing EN methodologies to freshwater ecosystem conservation. Most EN studies have been developed for terrestrial ecosystems, focusing on connectivity challenges such as habitat fragmentation caused by urban expansion or road network [41,42], and thus often overlook hydrological processes critical to freshwater environments. As a result, these approaches tend to treat freshwater ecosystems similarly to terrestrial landscapes, with limited consideration of water-mediated transport, longitudinal flow dynamics, or watershed-scale connectivity [43]. This often leads to an overestimation or underestimation of freshwater corridors connectivity, or corridors that erroneously span freshwater and terrestrial patches, contradicting species dispersal patterns. Some studies have attempted to consider freshwater systems independently; for instance, Wei et al. (2023) equally considered aquatic (provisioning, supporting, regulating, and cultural services) and terrestrial ecosystem services (water retention, soil conservation, and biodiversity maintenance) in source area identification [44]; Yang et al. (2025) first identified aquatic and terrestrial source areas separately and then developed distinct sets of resistance factors for each: for aquatic sources—elevation, distance to water systems, and NDVI; for terrestrial sources—slope, atmospheric diffusion rate, and road accessibility [45]. However, even when freshwater systems are considered independently, current EN frameworks rarely integrate both structural modifications and functional disruptions—such as degraded water quality and hydraulic infrastructure barriers—that influence aquatic species movement, especially in anthropogenically altered watersheds [26,46]. Therefore, there is a pressing need to develop specialized EN approaches tailored to freshwater ecosystems, incorporating indicators that can simultaneously assess multi-dimensional hydrological connectivity under human-induced stressors.
Therefore, there is a pressing need to develop specialized EN approaches tailored to freshwater ecosystems [27]. The unified identification approach often produces errors in freshwater–terrestrial environments because it applies the same resistance values and connectivity metrics to fundamentally different movement mechanisms—water-mediated transport versus terrestrial dispersal—leading to corridors that span incompatible habitat types and fail to reflect species-specific movement behaviors. These approaches must employ different evaluation factors for the distinct factors affecting species migration in freshwater versus terrestrial ecosystems. Simultaneously, in freshwater systems, the special nature of hydrological connectivity must be considered, especially under human-induced stressors such as altered flow regimes, water quality degradation, and hydraulic infrastructure barriers. By developing a framework that integrates both habitat quality indicators and hydrological processes, a comprehensive approach for EN evaluation in aquatic–terrestrial interface zones can be provided.
In response to this need, this study addresses two critical research gaps: (1) existing EN frameworks have largely overlooked the distinct characteristics of freshwater ecosystems and lack dedicated metrics for accessing hydrological connectivity; and (2) conventional hydrological connectivity assessments insufficiently account for functional disruptions caused by water quality and hydraulic infrastructures in anthropogenically modified watersheds. To bridge these gaps, this research aims to (1) develop freshwater-specific ecological source identification methods incorporating water quality and habitat suitability indicators; and (2) construct resistance surfaces that reflect both physical barriers and functional disruptions to support the extraction of effective freshwater corridors. The proposed framework seeks to offer ecologically meaningful and spatially explicit guidance for watershed freshwater ecosystem conservation, particularly in complex terrestrial–freshwater coupled systems where conventional EN methods fall short in capturing the compounded impacts of human modifications on hydrological connectivity.

2. Materials and Methods

2.1. Study Area

In this study, we selected the Yangtze River Delta’s Ecological Green Integration Demonstration Zone as our study area (Figure 1). Spanning Qingpu District of Shanghai, Wujiang District of Suzhou (Jiangsu Province), and Jiashan County of Jiaxing (Zhejiang Province), it covers a total land area of 2413 km2 (31°17′24″–31°45′36″ N, 120°21′36″–121°19′48″ E). Located on the eastern edge of the Taihu Lake Basin, the region is characterized by flat terrain crisscrossed by a dense river network, with water bodies totaling 438.2 km2 (18.16% of the total area). This region represents a typical plain river network area where terrestrial and freshwater ecosystems are intricately interwoven, making it particularly suitable for testing our dual network framework due to its high connectivity and significant human disturbances.
In addition, rapid urban expansion has increasingly fragmented habitats and—because freshwater ecosystems depend on uninterrupted flow paths—has produced even more pronounced degradation in freshwater environments. Frequent flood events have led to the widespread construction of hydraulic infrastructures, which has substantially altered hydrological connectivity and disrupted freshwater ecosystem integrity. At the same time, large-scale hydraulic infrastructure built for flood control, irrigation, and water supply has altered flow regimes and reduced rivers’ natural self-purification capacity, while extensive industrial activities along waterways have further intensified pollution inputs. Together, these pressures have accelerated ecosystem degradation and exacerbated local water-environment challenges, creating an ideal testing ground for evaluating frameworks that address both terrestrial and freshwater connectivity under anthropogenic stress.

2.2. Data Sources

The study employed five primary datasets: land use data, the Normalized Difference Vegetation Index (NDVI), water-system distribution data, the Normalized Difference Water Index (NDWI), and sluice location data. Table 1 summarizes their key attributes.
Land use data were obtained from the Resource Environmental Science and Data Platform but, owing to limited accuracy in distinguishing forest and water bodies, were corrected using contemporaneous ESA land-cover data. We classified land use/land cover into ten categories: dryland, paddy field, grass, woodland, lake, pond, river, urban land, rural settlement, and other construction land. Finally, all datasets were harmonized to a common spatial resolution of 30 m × 30 m.

2.3. Methods

The methodological framework of this study proceeded through four integrated steps (Figure 2). First, we processed the NDVI and NDWI through the InVEST model to generate habitat quality layers for terrestrial and freshwater systems, then applied MSPA to delineate high-value ecological sources. Second, we produced the river network resistance surface by computing Manning-based resistance for each river reach, weighting sluice effects, and assigning these values to grid cells. We then applied the Minimum Cumulative Resistance (MCR) model to calculate least-cost paths between source patches, explicitly accounting for differences in dispersal behaviors between terrestrial and aquatic species. Finally, we applied circuit theory to each resistance surface to delineate corridors, then combined these with their ecological sources to form the unified freshwater and terrestrial EN.

2.3.1. Sources Identification

Ecological sources are key habitat patches that sustain biodiversity and maintaining landscape connectivity [47]. This study identified ecological sources by integrating structural and functional connectivity assessments: MSPA was used for structural connectivity, while the habitat quality module of the InVEST model assessed functional connectivity. The latter evaluates habitat degradation based on the type and proximity of threat sources and the sensitivity of each habitat type to those threats [48,49].
The InVEST model parameters were set following the threat source scoring standards outlined in the InVEST User Guide (https://naturalcapitalproject.stanford.edu/software/invest (accessed on 21 March 2023)), based on known impacts of threat factors on habitat quality to ensure model outputs correspond with actual ecological environmental changes. Given the distinct characteristics of terrestrial and freshwater ecosystems, we applied differentiated parameter sets for each system type (Supplementary Table S1). Threat sources were defined as construction areas (including urban land, rural settlement, and construction land) and roads (including expressway, primary and secondary road, and tertiary road) (Supplementary Figure S1), with their impact distances and decay functions determined according to empirical studies in similar plain river network regions [50,51]. Habitat sensitivity scores were assigned based on land cover types and ecosystem-specific vulnerabilities, with natural habitats (forests, wetlands, water bodies) receiving high sensitivity scores to their respective threat types, while human-modified habitats (dry land, paddy field) received low sensitivity scores [52].
To improve accuracy across different ecosystem types, we incorporated additional habitat-quality indicators that have been extensively validated in ecological network studies. For terrestrial ecosystems, we combined InVEST’s habitat-quality output with the Normalized Difference Vegetation Index (NDVI), a widely used proxy for vegetation density and health that effectively reflects vegetation dynamics in ecosystem monitoring [42]. For freshwater ecosystems, we integrated InVEST results with the Normalized Difference Water Index (NDWI) to represent freshwater ecosystem integrity [43]. These spectral indices were selected over other available indices due to their superior universality and empirical validation, making them particularly suitable for large-scale, multi-dimensional ecological network analysis [53,54].
We then computed unified habitat quality scores using weighted averages with a 3:1 ratio (InVEST: 0.75, NDVI/NDWI: 0.25). The current weighting configuration was determined through iterative calibration, which was designed to ensure appropriate source patch sizes while avoiding ecological network fragmentation caused by overly fragmented habitats [55], thereby enhancing the functional connectivity and ecological stability of source areas. The dominant weight assigned to InVEST (75%) ensures that comprehensive threat-based habitat assessment remains the primary factor, while the supplementary spectral indices (25%) provide additional validation and refinement of habitat-quality patterns. The results were classified into five categories using the natural breakpoint method.
MSPA was applied to land use data to evaluate spatial structure and identify connectivity-relevant patterns [56]. Areas with the highest habitat quality were designated as the MSPA foreground, which was then classified into seven landscape types: Core, Islet, Perforation, Edge, Loop, Bridge, and Branch [57]. Among these, Core areas—representing the most critical habitat patches—were designated as ecological sources.
Therefore, ecological sources were determined separately for terrestrial and freshwater ecosystems based on high structural connectivity (MSPA) and high functional connectivity (habitat quality). These sources were further evaluated using the Probable Connectivity Index (PC) and the Patch Importance Index (dPC):
P C = ∑ i = 1 n ∑ j = 1 n a i ⋅ a j ⋅ p i j * A L 2
d P C = P C − P C r e m o v e P C × 100 %
where n denotes the total number of patches within the study area; a i and a j denote the area of patch i and patch j , respectively; p i j * represents the maximum probability of connectivity between patches i and j ; and A L denotes the overall area of the study patches.

2.3.2. Hydrological Connectivity Assessment

Hydrological connectivity underpins freshwater species dispersal in low-gradient plain river network regions, where widespread hydraulic infrastructure has disrupted natural flow regimes and fragments habitats. We therefore modeled the river system as a resistance network—combining natural flow impedance and anthropogenic barriers—to quantify movement constraints using graph theory.
(1)
River network generalization
The complex river network was abstracted as an undirected graph (Figure 3), with nodes representing junctions and terminal points, and edges corresponding to individual river reaches [58,59]. An undirected representation was adopted because the flat topography and tidal influences in this low-gradient plain river network region give rise to bidirectional currents and ambiguous stream hierarchies, allowing species to move in either direction along river reaches [59].
(2)
Flow resistance value
Flow resistance is a fundamental measure of hydrological connectivity, reflecting how channel morphology and flow dynamics jointly influence river evolution and freshwater species dispersal [60]. In our study area, we simplified the friction slope S [61] and computed resistance H for each river reach using Manning’s equation for trapezoidal channels. The hydraulic radius R is defined as
R = A P
where
A = b h + m h 2 , P = b + 2 h 1 + m 2
Combined this with Manning’s equation yields the flow resistance H :
H = l v = n l R − 2 3 = n l [ b + m h   h b + 2 h 1 + m 2 ] − 2 3
where   l   ( m ) is the flow path length, v   ( m · s − 1 )   the mean velocity,   b   ( m ) the channel bottom width,   h   ( m ) the flow depth, n   ( s · m − 1 / 3 )   the Manning’s roughness coefficient, and   m   the side-slope ratio. Higher H values indicate stronger flow impedance and thus lower connectivity for species movement [48].
The channel morphological parameters (bottom width, flow depth, and flow path length) for all river reaches in the study area are provided in Supplementary Table S2.
(3)
Resistance value setting
Sluices impede both water flow and freshwater-species dispersal, thereby elevating hydrological resistance [50]. We first georeferenced sluices using Water Resources Department maps (Figure 3) and assigned each to its river reach. To quantify the differential impacts of sluice infrastructure on hydrological connectivity, resistance multipliers were determined through iterative calibration that balanced structural and functional connectivity considerations.
Primary sluices (structures on administrative river courses) were assigned a multiplier of 1.5, while secondary sluices (structures on non-administrative river courses) received a multiplier of 1.25, reflecting their respective impacts on longitudinal connectivity. These coefficients were determined by testing multiple combinations (ranging from 1.1 to 2.0 for primary and 1.05 to 1.5 for secondary sluices) and selecting values that (1) appropriately reflected the barrier effects of different sluice levels on freshwater species movement and (2) avoided over-amplification in high-density sluice areas that could imbalance structural and functional connectivity assessments.
Natural resistance values were multiplied by these calibrated factors, with effects compounding additively on reaches hosting multiple sluices. This produced a unified resistance surface integrating natural flow impedance and anthropogenic barriers to connectivity.

2.3.3. Resistance Surface Design

Resistance surfaces translate heterogeneous landscape features into movement costs for species [62], which we quantified using the MCR model. For each grid cell i, the MCR value is
M C R = f m i n ∑ j = n i = m ( D i j × R i )
where D i j denotes the spatial distance from ecological source j to cell i , and R i denotes the resistance coefficient of cell   i to species migration.
Recognizing that species movement mechanisms differ fundamentally between terrestrial and freshwater ecosystems, we built separate resistance surfaces. For terrestrial ecosystems, we combined four weighted resistance factors—land use types, MSPA landscape types, the NDVI, and InVEST habitat quality—into a single surface [63]. Factor weights were derived via the Analytic Hierarchy Process (AHP) [64], achieving consistency ratios (CRs) below 0.1 (Supplementary Table S2). Each factor was reclassified onto a logarithmic scale from 100 (suitable habitats) to 104 (barriers) [55], and their weighted sum yielded the terrestrial ecosystem resistance surface. For freshwater ecosystems, we merged the hydrological connectivity resistance surface (developed in Section 2.3.2) with resistance factors including land use, MSPA, the NDWI, and InVEST habitat quality [54,65]. This dual integration ensures that freshwater species movement costs reflect barriers arising from both structural connectivity (landscape configuration) and functional connectivity (hydraulic resistance and water-quality impacts).

2.3.4. Ecological Corridor Identification and Classification

Circuit theory treats a landscape as an electrical network—where resistance values impede current flow and areas with high current density indicate critical movement pathways [66]. Using ArcGIS 10.2 with the Linkage Mapper 2.0.0 plug-in, we simulated species random movement patterns as electrical currents flowing through resistance surfaces [67]. This method naturally identifies multiple, alternative routes between source patches and inherently accounts for landscape heterogeneity through the underlying resistance values. Moreover, by ranking corridors according to current density, it provides a hierarchical prioritization of movement pathways [68]. Finally, we classified the resulting corridors for each ecosystem type into priority tiers using the natural breakpoint method. This yield two ENs—one terrestrial, one freshwater—each with corridors ordered by their relative importance for species movement.

3. Results

3.1. Extent, Configuration, and Spatial Patterns of Freshwater and Terrestrial Ecological Sources

Freshwater and terrestrial ecological sources differ markedly in extent, configuration, and spatial clustering. We delineated 78 freshwater patches covering 27,195.61 ha (11.82% of the study area), versus 100 terrestrial patches totaling 12 157.96 ha (5.29%). Freshwater sources are larger on average (mean 348.66 ha) and cluster tightly around major lakes—Taihu, Dianshan, Yuandang, and Beima—reflecting these lacustrine systems’ role as core dispersal hubs. In contrast, terrestrial patches are smaller (mean 121.58 ha), more widely dispersed across woodlands in southern Wujiang and the agricultural belts surrounding Taihu Lake and Beimayang Lake (Figure 4). This pattern reflects a fragmented mosaic of terrestrial ecological sources rather than contiguous strongholds. Connectivity classification further highlights these contrasts. Among freshwater patches, 32 primary sources (mean size 561.54 ha) account for 66.07% of freshwater ecological sources, with the remaining 46 secondary sources averaging 200.62 ha. Terrestrial systems include 33 primary sources (mean size 209.40 ha; 56.81% of terrestrial sources) and 67 secondary sources (mean size 78.32 ha).
Within terrestrial sources, agroforestry-complex patches—rice paddies interlaced with forest patches—cover 2885.64 ha (41.75% of primary terrestrial sources) (Figure 4). Although not directly measured in our work, the functional role of these mosaics is determined from the literature: by weaving edges and corridors, they create structural heterogeneity and functional connectivity. These mosaics link habitat fragments and sustain ecosystem services across the study area [69].

3.2. Hydrological Connectivity and Resistance Values

Resistance values were classified into five exponential classes: Level 1 comprises large lakes and ponds with the lowest resistance; levels 2 through 4 cover river reaches with low (3.38–55.47), medium (55.47–253.12), and high (253.12–880.85) resistance; and level 5 corresponds to narrow internal drainage networks with the highest resistance. To reflect their complete barrier effect on hydrological connectivity, all terrestrial areas were uniformly assigned the maximum resistance.
At the district scale, hydrological connectivity varies sharply (Figure 5). Wujiang District exhibits optimal connectivity, as major waterways such as the Taipu River and the Grand Canal remain in level 1–2 (<55.47), forming continuous low-resistance corridors that facilitate freshwater species migration and maintain ecosystem connectivity. In Qingpu District, mixed resistance patterns yield moderate connectivity overall. By contract, Jiashan County’s dense sluice network and narrow channel cross-sections push many reaches into levels 3–5 (55.47–880.85), creating pronounced flow barriers that impede natural regimes and species movement.
These connectivity patterns closely mirror ecological-source distributions (Figure 5): areas dominated by low-resistance channels—particularly in Wujiang and southwestern Qingpu—support extensive primary source patches, whereas high-resistance zones fragment sources into isolated “islands”. This fragmentation is most acute in the northeastern Jiaxing–Qingpu transition, which acts as a critical bottleneck, undermining network cohesion and limiting species dispersal between major habitat cores.

3.3. Number, Length, and Spatial Patterns of Freshwater and Terrestrial Ecological Corridors

Through overlaying ecological sources with resistance surfaces (Figure 6), we mapped 363 potential ecological corridors spanning a total length of 1111.13 km, of which 160 freshwater corridors total 456.35 km and 203 terrestrial corridors total 658.79 km (Figure 7). Despite their greater number, terrestrial corridors do not necessarily offer better connectivity: they arise from numerous, small, and dispersed ecological sources and often cut across high-resistance urban areas. In contrast, freshwater corridors closely follow the existing river network, yielding a more uniform distribution and stronger alignment with natural flow paths—attributes that confer superior structural integrity and functional coherence at the landscape scale.
Corridor classification further underscores the primacy of aquatic pathways: 96 of 160 freshwater corridors (42.5%) qualify as primary corridors, demonstrating high functional effectiveness for species movement. Terrestrial corridors, in comparison, exhibit a lower proportion of primary corridors (36.45%), reflecting their fragmented topology and variable resistance (Table 2). These findings highlight freshwater corridors as the modeled backbone of regional ecological connectivity and identify terrestrial network segments where targeted restoration or corridor enhancement is most needed. This classification is grounded in corridor length and resistance metrics derived from model outputs, rather than direct ecological validation through species movement or genetic evidence.

4. Discussion

4.1. Distinct Assessments of Freshwater and Terrestrial Ecosystem Connectivity

Maintaining landscape connectivity is fundamental to biodiversity conservation [9], particularly in mixed landscapes such as lowland deltas and floodplain networks where terrestrial and freshwater ecosystems are intricately interwoven [20,36]. However, most ecological network studies apply unified approaches that fail to distinguish between the fundamentally different connectivity mechanisms governing these two ecosystem types [43]. Our results demonstrate that freshwater and terrestrial corridors exhibit distinct spatial patterns and connectivity characteristics in fragmented landscapes. Terrestrial corridors typically show clustered, patch-like distributions, with an average corridor length of 3.24 km and a higher corridor density within localized habitat groups. However, longer terrestrial corridors, which attempt to bridge isolated patches across high-resistance urban matrices, often fail to maintain effective connectivity—reflected by their lower continuity and reduced proportion of primary corridors (36.45%). In contrast, freshwater corridors are generally longer, with an average length of 4.46 km, and more uniformly distributed along natural river networks. This linear alignment allows them to bypass fragmented terrestrial landscapes and sustain higher proportions of primary corridors (42.50%), providing more consistent watershed-scale connectivity. Moreover, freshwater sources also tend to be larger in size (average 348.66 ha vs. 121.58 ha in terrestrial sources) and contribute higher dPC values, indicating their central role in maintaining overall ecological network structure.
Compared to terrestrial ecosystems, where connectivity assessments often focus on the static spatial configuration of habitat patches, freshwater ecosystem connectivity is governed not only by physical spatial arrangement but also by dynamic hydrological conditions and ecological processes. In particular, hydraulic infrastructure—such as dams, sluices, and gates—plays a crucial role in shaping hydrological connectivity. These structures alter flow velocity, disrupt sediment transport, and act as physical barriers to aquatic species migration, thereby significantly modifying both structural and functional connectivity. Consequently, restoring connectivity in freshwater ecosystems requires more than spatial optimization of habitat patches—it demands an understanding of flow dynamics and the incorporation of anthropogenic influences into connectivity models. This highlights the need for dynamic, infrastructure-aware approaches that reflect real-world conditions in human-modified waterscapes.
The reason for the significant differences between the two types of corridors lies in the fundamentally distinct connectivity mechanisms between the two ecosystems: terrestrial ENs rely on topography, vegetation cover, and human activity patterns, whereas freshwater ENs must account for hydrological connectivity, water quality variations, and hydraulic infrastructure barriers. Applying unified, terrestrial-oriented models to mixed landscapes often results in misaligned corridor designs that fail to respect species-specific movement pathways—for example, generating terrestrial corridors that cross water bodies or connect to aquatic ecological sources—or simultaneously overlooking critical small-scale terrestrial refugia [70,71,72]. Our dual framework addresses these limitations by implementing ecosystem-specific criteria and parameters. For freshwater systems, we coupled NDWI-InVEST indicators with sluice-adjusted resistance surfaces, producing corridors that align with high-connectivity river networks. For terrestrial systems, our approach successfully identified numerous small but high-quality agroforestry-complex patches that unified models frequently overlook [63,69]. Overall, these comparisons confirm that separately constructing freshwater and terrestrial ENs is essential for accurately mapping species dispersal corridors and for uncovering critical stepping stones in mixed landscapes.

4.2. Watershed-Scale Hydrological Connectivity Under Intensive Anthropogenic Disturbance

Maintaining hydrological connectivity across entire watersheds is crucial for freshwater biodiversity conservation, yet most current efforts rely on species monitoring and field surveys confined to individual river reaches or specific water bodies [73]. Such localized studies—tracking population dynamics, community composition, distribution ranges, and water-quality parameters—cannot capture connectivity at the scale of low-gradient, heavily modified plain river networks. Consequently, there is a pressing need for basin-wide assessment tools that integrate multiple data streams, including river reaches morphology and habitat quality.
Structural connectivity methods—primarily based on graph theory—have been widely applied to overcome spatial scale limitations by representing freshwater ecosystems as networks of nodes (habitat patches) and links (flow pathways) [58,59]. However, these approaches typically assume undisturbed river topology and therefore fail to account for hydraulic barriers such as sluices and dams that fragment habitats. Functional connectivity studies, on the other hand, demonstrate that hydraulic infrastructure exerts profound negative effects on migratory species: sluices obstruct spawning and feeding migrations, reduce gene flow, and decrease both abundance and diversity [74]. Yet by focusing on barrier impacts in isolation, these works overlook the broader network context—how localized impediments interact with upstream and downstream pathways to shape watershed-scale connectivity.
Together, the shortcomings of purely structural or purely functional approaches leave watershed-scale connectivity undercharacterized in landscapes dominated by dense river networks and intensive infrastructure. To bridge this gap, we propose a hybrid framework that embeds hydraulic barrier data into graph-theoretic network indicators, thereby uniting structural topology with barrier resistance. This method requires only readily available engineering distribution maps and basic habitat quality indicators, making it suitable for data-limited regions.
Our approach (1) constructs a generalized river network topology from hydrographic data; (2) incorporates sluice and dam locations—weighted by their obstruction severity—directly into link-resistance values; and (3) applies network analysis (e.g., connectivity indices, least-resistance path extraction) to reveal both intact flow corridors. Compared with prior ecosystem service-based methods [45] that misclassified Jiashan’s sluice-dense regions as highly connected, our framework correctly assigns lower connectivity to these segments. By balancing structural and functional dimensions, we deliver a comprehensive, watershed-scale hydrological connectivity assessment that can guide conservation planning in heavily modified plain river systems [19,60].
While our hybrid framework successfully captures the general patterns of hydrological connectivity in heavily modified watersheds, several methodological considerations warrant further investigation. The fixed resistance multipliers we applied to quantify the impacts of sluice infrastructure represent average barrier effects but cannot account for operational variability—many sluices in our study area operate seasonally or during flood events, creating temporal windows of enhanced connectivity that our static assessment overlooks [11,27]. Similarly, our resistance surfaces, while effective for identifying broad connectivity patterns, do not distinguish between aquatic species with different swimming capabilities or habitat requirements. For instance, small resident species may be more severely affected by low-flow conditions around sluices, while larger migratory species might overcome barriers during high-flow periods [75,76]. Future applications could benefit from incorporating seasonal hydrological data and species-specific movement studies, particularly in regions where detailed ecological monitoring data are available to refine barrier permeability estimates.
By balancing structural and functional dimensions while acknowledging these limitations, our framework provides a practical starting point for comprehensive, watershed-scale hydrological connectivity assessment that can guide regional conservation planning in heavily modified plain river systems.

4.3. Application Potential of the Dual-Network Framework Under National Ecological Planning Contexts

The proposed dual-network framework for freshwater–terrestrial connectivity is closely aligned with China’s national ecological security strategy and directly supports the goals outlined in the Master Plan for National Key Ecosystem Protection and Restoration Major Projects (2021–2035), which emphasizes integrated watershed management, ecological corridor construction, and the protection of key ecological spaces [77].
The plan incorporates the Permanent Basic Farmland (PBF) Protection Redline into the “three control lines” of territorial spatial planning, alongside the Ecological Conservation Redline and the Urban Development Boundary, providing an institutional foundation for ecological restoration and land use planning [77]. Based on this, our framework suggests integrating small terrestrial sources into the PBF and associated green infrastructure networks—such as agroforestry systems, urban greenways, and park systems—to enhance terrestrial ecological network integrity. Concurrently, in accordance with the plan’s emphasis on wetland protection and restoration, small freshwater sources can be incorporated into designated wetland protection zones to strengthen the ecological functionality and connectivity of freshwater ecosystems.
In terms of watershed-scale restoration, the national plan calls for comprehensive governance in major basins such as the Yangtze and Yellow Rivers. Our dual-network framework provides an applied pathway to support these efforts by recommending the strategic removal of obsolete hydraulic structures and the implementation of ecological bank restoration, thereby repairing hydrological and ecological connectivity in freshwater corridors and alleviating legacy barriers to biodiversity conservation [60,74].
This framework is particularly well-suited for regional ecological network planning in areas characterized by coexisting hydraulic infrastructure and landscape fragmentation. It offers actionable guidance for urban development regions with mixed freshwater–terrestrial landscapes. By facilitating the integrated implementation of ecosystem conservation and restoration strategies, the dual-network approach advances beyond traditional terrestrial ecological network methods, improving the identification of connectivity in human-modified watersheds and providing a more systematic and science-based foundation for functional landscape planning and national biodiversity conservation.

4.4. Limitation and Future Research Directions

The Yangtze River Delta is one of China’s most urbanized and densely populated regions, where terrestrial habitats are highly fragmented and freshwater systems suffer from degraded water quality and pervasive hydraulic infrastructure. While our dual-framework method advances watershed-scale connectivity assessment by integrating sluice impacts, incorporating water quality and habitat suitability into freshwater-specific source identification and constructing resistance surfaces that reflect both physical barriers and functional disruptions further enhance the accuracy and ecological relevance of freshwater corridor extraction.
First, by treating freshwater and terrestrial sources and resistance surfaces separately, we more accurately generated ENs that reflect species-specific dispersal patterns and successfully identified high-value, small-area AFs. However, this separate identification approach also introduced new challenges: freshwater and terrestrial resistance surfaces were constructed independently and overlaid spatially, without fully accounting for the unique ecological dynamics and interactions at aquatic–terrestrial interfaces. Recent studies have emphasized the importance of accounting for edge effects in ecological network (EN) assessments. For example, Xie et al. (2024) incorporated shared edges between forestland and paddy fields into their resistance modeling and demonstrated that these transitional zones provided the lowest resistance to avian movement [78]. Similarly, Yang et al. (2025) highlighted strong synergistic relationships between aquatic and terrestrial ecosystem services in ecotonal areas, underscoring their ecological significance [45]. Our approach, however, did not capture these edge effects—particularly crucial in lowland deltas where aquatic–terrestrial coupling gives rise to high-value habitats. Future research should explore how to incorporate edge effects into habitat quality assessments or resistance surface construction. Second, our freshwater ecosystem resistance surface relies primarily on NDWI–InVEST habitat-quality indicators and sluice-adjusted river reaches resistance; it does not yet incorporate biotic habitat-quality indicators such as in-stream vegetation density, substrate type, or other aquatic ecological factors that directly influence species habitat preferences and movement patterns [79,80].
Third, most EN studies validated functional connectivity using species distribution or movement data [78]. In small-scale studies, field observations across multiple taxonomic groups—such as birds, amphibians, and fish—are often available, enabling comprehensive, multi-species validation [81]. However, in large-scale regions like our study area, empirical data are typically limited, with citizen-science bird observations serving as the primary source. Several studies have pointed out the spatial sampling bias inherent in such data, which are predominantly collected in easily accessible urban and roadside areas. In contrast, high-quality ecological resources and critical migratory corridors—such as Dianshan Lake and Yuandang—are mainly located in rural zones, where observations are sparse or absent [78]. As a result, relying solely on bird-based citizen-science data for validation risks underestimating the functionality and connectivity of key ecological corridors. To address this limitation, our study proposes a proxy-based indicator framework for EN construction in data-scarce, large-scale regions. Nevertheless, the robustness of this approach remains to be empirically tested. Future research should incorporate systematic multi-taxon biodiversity surveys, particularly for aquatic and semi-aquatic species, to validate and refine the accuracy and ecological relevance of the proposed networks.
Finally, due to the sparse distribution of hydrological monitoring stations and limited data availability, this study faced constraints in data acquisition. The study area, which previously experienced rapid urbanization, has recently become a key ecological protection zone, with land use changes slowing under the influence of urban planning policies. As a result, our analysis integrated data from the year 2022, capturing spatial heterogeneity but lacking temporal variation—thus representing a static assessment. Future research should incorporate longer time series and higher-resolution multi-source datasets to enable a more comprehensive analysis that integrates dynamic land use transitions and hydrological changes.
To address these limitations, future research should develop edge-coupling resistance models, expand habitat-quality indicators to include biotic factors, and implement multi-taxon monitoring programs using eDNA sampling and acoustic telemetry [82].

5. Conclusions

This study demonstrates that separate assessments of freshwater and terrestrial landscapes are essential for accurately characterizing connectivity in composite freshwater–terrestrial ecosystems. By developing ecosystem-specific resistance surfaces—incorporating water-quality indicators, hydrological connectivity factors, and hydraulic barriers—we revealed contrasting network patterns. For freshwater ecosystems, 78 ecological sources, averaging 348.66 ha and clustered around major lakes such as Taihu and Dianshan, form extensive and continuous corridors totaling 456.35 km, with 42.50% classified as primary, reflecting high functional connectivity along natural river networks. In contrast, terrestrial ecosystems consist of 100 ecological sources, with an average area of 121.58 ha, scattered across fragmented woodlands and agricultural belts in southern Wujiang District. Agroforestry-complex patches account for 41.79% of the primary terrestrial sources and 23.74% of the total terrestrial sources. These sources are interconnected by 658.79 km of corridors (36.45% as primary) and are numerous but often disrupted by urban areas, with a lower proportion of primary links. This dual-framework approach captures watershed-scale connectivity more effectively than unified models, providing a quantitative basis for targeted conservation planning.
Although our dual-framework reveals contrasting network patterns, it relies on independently overlaid resistance surfaces and lacks empirical validation; its practical effectiveness in guiding conservation actions remains to be further tested and optimized, particularly in terms of its performance across multiple taxonomic groups. Future efforts should couple freshwater and terrestrial surfaces at their interfaces, integrate additional habitat indicators (e.g., vegetation density, substrate composition), and establish watershed-scale multi-taxon monitoring for robust validation. By doing so, this refined approach will deliver more accurate connectivity assessments and guide adaptive management of hydraulic infrastructure and targeted habitat restoration. Ultimately, our methodology offers a practical, data-driven tool for sustaining biodiversity and ecosystem services within rapidly urbanizing freshwater–terrestrial landscapes.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/land14081562/s1; Figure S1: Threat factors; Table S1: InVEST parameters; Table S2: Resistance Values and Resistance Surface Weight.

Author Contributions

Conceptualization, Y.X., X.S., T.C. and Q.S.; methodology, T.C., Q.S. and Y.Z.; software, T.C. and Q.S.; validation, T.L.; investigation, T.L. and Y.Z.; writing—original draft preparation, T.C. and Q.S.; writing—review and editing, Y.X. and X.S.; visualization, Y.X.; supervision, Y.X.; project administration, T.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Natural Science Foundation of Zhejiang Province, grant number LQ21E080016; and the Center for Balance Architecture, Zhejiang University, grant number KH-20212740.

Data Availability Statement

Data are contained within the article; further inquiries can be directed to the corresponding author.

Conflicts of Interest

Author Xiaoming Shen was employed by the company The Architectural Design & Research Institute of Zhejiang University Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Geographic location of the Yangtze River Delta’s Ecological Green Integration Demonstration Zone.
Figure 1. Geographic location of the Yangtze River Delta’s Ecological Green Integration Demonstration Zone.
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Figure 2. Research framework.
Figure 2. Research framework.
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Figure 3. River network hierarchy and associated sluice distribution.
Figure 3. River network hierarchy and associated sluice distribution.
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Figure 4. Distribution of freshwater and terrestrial ecological sources.
Figure 4. Distribution of freshwater and terrestrial ecological sources.
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Figure 5. Resistance values of generalized river reaches.
Figure 5. Resistance values of generalized river reaches.
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Figure 6. Resistance surface design for freshwater and terrestrial ecosystems.
Figure 6. Resistance surface design for freshwater and terrestrial ecosystems.
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Figure 7. Unified freshwater–terrestrial ecological network in the study area.
Figure 7. Unified freshwater–terrestrial ecological network in the study area.
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Table 1. Data sources.
Table 1. Data sources.
Data NameSourceAccuracyYear
Land use dataResource Environmental Science and Data Platform, https://www.resdc.cn/, European Space Agency (ESA), https://www.esa.int/30 m2020
Normalized Difference Vegetation Index (NDVI)Landsat 8/9 derived from satellite remote sensing imagery30 m2022
Water-system distribution dataTerritorial Spatial Master Plan for Demonstration of ecological green integration development in Yangtze River Delta (2021–2035), http://www.jiashan.gov.cn; Shanghai River and Lake Report (2022), https://www.shqp.gov.cn; Wujiang District Geological Hazard Prevention and Control Plan, http://www.wujiang.gov.cn/zgwj/slsw/xxgk_list.shtml
(accessed on 13 March 2023)		1:50,0002022
Sluices data–2022
Normalized Difference Water Index (NDWI)Landsat 8/9 derived from satellite remote sensing imagery30 m2022
Table 2. Statistics of ecological sources and corridors in freshwater and terrestrial ecosystems.
Table 2. Statistics of ecological sources and corridors in freshwater and terrestrial ecosystems.
CategoryFreshwater EcosystemTerrestrial Ecosystem
Source
Number of Ecological Sources78100
Number of Primary Sources32 (66.07%)33 (56.81%)
Average Area of Primary Sources (ha)561.54209.40
Average dPC of Primary Sources0.100.08
Number of Secondary Sources4667
Average Area of Secondary Sources (ha)200.6278.32
Average dPC of Secondary Sources0.02720.0222
Total Area of Ecological Sources (ha)27,195.61 (11.82%)12,157.96 (5.29%)
Average Area of Ecological Sources (ha)348.66121.58
Corridor
Number of Ecological Corridors160203
Total Length of Ecological Corridors (km)456.35658.79
Proportion of Primary Corridors42.50% (96 corridors)36.45% (74 corridors)
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Cai, T.; Shi, Q.; Luo, T.; Zheng, Y.; Shen, X.; Xie, Y. Separate Versus Unified Ecological Networks: Validating a Dual Framework for Biodiversity Conservation in Anthropogenically Disturbed Freshwater–Terrestrial Ecosystems. Land 2025, 14, 1562. https://doi.org/10.3390/land14081562

AMA Style

Cai T, Shi Q, Luo T, Zheng Y, Shen X, Xie Y. Separate Versus Unified Ecological Networks: Validating a Dual Framework for Biodiversity Conservation in Anthropogenically Disturbed Freshwater–Terrestrial Ecosystems. Land. 2025; 14(8):1562. https://doi.org/10.3390/land14081562

Chicago/Turabian Style

Cai, Tianyi, Qie Shi, Tianle Luo, Yuechun Zheng, Xiaoming Shen, and Yuting Xie. 2025. "Separate Versus Unified Ecological Networks: Validating a Dual Framework for Biodiversity Conservation in Anthropogenically Disturbed Freshwater–Terrestrial Ecosystems" Land 14, no. 8: 1562. https://doi.org/10.3390/land14081562

APA Style

Cai, T., Shi, Q., Luo, T., Zheng, Y., Shen, X., & Xie, Y. (2025). Separate Versus Unified Ecological Networks: Validating a Dual Framework for Biodiversity Conservation in Anthropogenically Disturbed Freshwater–Terrestrial Ecosystems. Land, 14(8), 1562. https://doi.org/10.3390/land14081562

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