Recreational Fisheries Encountering Flagship Species: Current Conditions, Trend Forecasts and Recommendations

Abstract

Recreational fisheries increasingly intersect with the habitats of flagship species, i.e., species that attract public attention and drive conservation efforts, raising potential ecological conflicts. This study investigated the spatial coupling between recreational fisheries and three flagship species in the Yangtze River Basin: the Chinese alligator (Alligator sinensis), the Yangtze finless porpoise (Neophocaena phocaenoides), and the scaly-sided merganser (Mergus squamatus). Drawing on over 10,000 fishing Points of Interest recorded between 2015 and 2024 and over 300 verified species occurrences, this study applied a Random Forest model with spatial integration and a Maximum Entropy model to examine estimated current distributions and forecast interactions from 2025 to 2035. Flagship species habitat suitability was modeled and projected at a spatial resolution of 1 km, while recreational fishing density was resolved on a coarser grid of 1.875° × 1.25° in latitude–longitude dimensions. Results reveal a substantial increase in high-risk overlap zones. For example, high-density fishing areas within high-suitability habitats for the scaly-sided merganser expanded from 0 km² in 2015 to 85,359 km² in 2024. Projections indicate continued intensification of such overlaps, particularly in regions including Ma’anshan–Wuhu, the Taihu–Chaohu–Poyang lake system, and Yibin. These findings offer robust, model-driven evidence of growing spatial conflicts and offer actionable insights for ecosystem-based governance. The methodological framework is transferable and supports broader applications in other regions and species under ecological sustainability goals.

1. Introduction

A flagship species is a high-profile, influential species that captivates public interest, thereby helping to raise awareness of environmental issues and drive conservation efforts [1]. Environmental organizations promote these species to engage the public, often selecting them for their appeal, familiarity, and endangered status [2]. As part of this strategy—known as the flagship species paradigm—these species are not only used as symbolic representatives but are also made accessible to the public, often through tourism and fundraising initiatives [3]. While effective for fundraising and awareness, this strategy has also raised concerns about the welfare of these species, especially when their habitats are commercialized for tourism and donor engagement [4,5].

In 2024, an individual of the gray wolf (Canis lupus chanco), one of the flagship species in China’s alpine ecosystems, was fatally struck in Hoh Xil after becoming habituated to human presence through prolonged feeding and roadside viewing [6]. This incident triggered public concern, underscoring the heightened vulnerability of flagship species—driven by their public appeal—to behavioral changes, reduced vigilance, and stress, all induced by feeding and close human observation. Such concern may also intensify support for fortress-style conservation, a strategy that prioritizes the strict exclusion of human activities to safeguard sensitive wildlife populations [7,8]. However, fully implementing such an approach can undermine the financial sustainability of protected areas, as many rely heavily on tourism revenue made possible by controlled public access to flagship species [7]. Thus, careful and context-sensitive management is necessary to enable the responsible governance of human–flagship species interactions.

While academic attention to these issues—particularly within aquatic ecosystem and freshwater species conservation—remains limited [9,10,11], field observations suggest that human–flagship species interactions are increasingly common in practice. In the Yangtze River Basin, for example, recent increases in sightings of the Yangtze finless porpoise (Neophocaena phocaenoides) [12], along with similar trends observed for the Chinese alligator (Alligator sinensis) [13] and the scaly-sided merganser (Mergus squamatus) [14], suggest that flagship species habitats are increasingly overlapping with areas of human recreational activity. In early 2024, a dead Yangtze finless porpoise was found in the Zhijiang section of the Yangtze River in Yichang, Hubei Province, with a fishing hook lodged in its mouth—an incident that triggered widespread public debate [15].

Previous studies have documented the direct ecological impacts of fishing activities on target species and their ecosystems from multiple perspectives, including (a) population declines due to overfishing [16], (b) shifts in age and size structures caused by selective harvesting [17], (c) increased mortality linked to catch-and-release practices [18], (d) threats to native species and habitats from introduced exotics [19], and (e) physical and chemical disturbances from frequent human activity [20]. In contrast, the impacts on non-target species have received considerably less attention, despite their potential to harm flagship species. Anglers may catch non-target species unintentionally [21], while lost or abandoned gear, such as broken lines and discarded nets, continues to entangle aquatic life and cause lasting harm [22]. Artificial feeding can further disrupt natural behavior and distribution, leading to nutritional imbalances, increased predation risk, and food dependency [23]. Moreover, the broader environmental and social consequences of harm to aquatic flagship species—such as public backlash, intensified conflicts with local fishers, or the marginalization of fishing communities—have received less attention in current research.

Building on these concerns, the rapid expansion of recreational fishing may represent a growing, yet underappreciated, source of disturbance to aquatic flagship species. Defined as the capture of aquatic animals not essential for subsistence with most catches excluded from formal markets, recreational fishery has grown substantially in recent years [24]. In China alone, the industry was valued at RMB 93.15 billion in 2023—an increase of 10.99% from the previous year—according to data from the National Aquatic Products Technology Extension Service and the Chinese Society of Fisheries [25]. Although recreational fishing can occur in private or controlled settings, it predominantly takes place in open, biodiverse natural waters that are ecologically suitable for long-term angling. However, the sustainability of these environments depends heavily on robust fish stocks and the integrity of aquatic ecosystems [26]. As recreational fishing grows in popularity and economic weight, it brings increasing pressure to reconcile ecological protection with human activity—especially in sensitive freshwater systems where flagship species are present [27,28,29]. The spatial extent and magnitude of recreational fishing’s disruption to flagship species remain poorly understood.

In the Chinese context, this issue is particularly salient: while the 14th Five-Year Plan for National Fisheries Development promotes the integration of recreational fishing with tourism and cultural industries, the Yangtze River Biodiversity Conservation Implementation Plan (2021–2025) prioritizes the protection of endangered aquatic species and key habitats. This policy tension highlights the urgent need to examine how flagship species and recreational fisheries interact spatially and functionally and to identify areas where management must balance conservation imperatives with recreational use.

Using data from 2015 to 2024, the present study explored the interactions between recreational fisheries and three flagship species in the Yangtze River Basin: the Chinese alligator, the Yangtze finless porpoise, and the scaly-sided merganser. The study also predicted flagship species encounter situations for 2025–2035 by categorizing their habitats into four suitability levels (unsuitable, low, medium, and high) and dividing the basin into four recreational fishery zones based on operational activity (non-recreational, low-density, medium-density, and high-density). The spatial overlap of these zones was then evaluated to estimate the probability of encounters. The results showed frequent interactions and encounters between flagship species and recreational fisheries, highlighting the need for focused research in this area. The study also provided geospatial decision-making support for managing these interactions and encounters.

2. Study Area, Materials, and Methods

2.1. Study Area: Yangtze River Basin

This study focused on the Yangtze River Basin, which is located in eastern China (90°34′ E to 122°11′ E, 26°03′ N to 35°33′ N) and spans Sichuan, Chongqing, Hubei, Hunan, Jiangxi, Anhui, Jiangsu, and Shanghai. This area covers 1.8 million km2 and houses over 400 million people. In addition, it features a diverse topography that ranges from 50 to 6000 m in elevation. The region is primarily influenced by temperate monsoon and subtropical humid climates.

This study examined the intersection of recreational fisheries and flagship species, since the region has been well-represented in studies of both subjects. The Yangtze River Basin is characterized by its rich biodiversity. For example, it hosts over 350 fish species, which account for more than one-third of the total freshwater fish species in China [30]. Furthermore, it is home to more than 400 species classified as endangered, according to the China Red List of Biodiversity [31]. The area is also renowned for its vibrant fishing culture and is characterized by a plethora of inland lakes [32], reservoirs, and rivers that serve as prime locations for recreational angling activities [33]. A map of the study area is shown in Figure 1.

2.2. Materials

2.2.1. Data for Recreational Fisheries

Recreational fishing, also known as sport or leisure fishing, encompasses a variety of forms, including angling, fish observation, and participation in fishing-related cultural activities [34]. In this study, the spatial distribution of recreational fishing activities was represented by the distribution of “fishing parks”, using them as a proxy indicator. The primary data source was geographic information obtained from Gaode Map (https://lbs.amap.com/ accessed on 30 December 2024) for the period 2015–2024. In Gaode’s database, “fishing parks” refer to publicly accessible recreational angling sites—such as managed ponds, lakes, or riverbank facilities—classified under leisure activity POIs (Points of Interest). These are identified based on business registration, location names, user-generated tags, and service-related features, and they typically represent areas with concentrated fishing activity. The changes in the number of POIs over this period are presented in

Table 1. Changes in the number of points of interest from 2015 to 2024.

Year	Number of POIs	Year	Number of POIs
2015	340	2020	5807
2016	518	2021	6433
2017	965	2022	7485
2018	1315	2023	8981
2019	4468	2024	10,777

.

To quantify the spatial intensity of recreational fisheries, the Yangtze River Basin was partitioned into 154 standardized grids (each measuring 1.875° × 1.25° in latitude and longitude), and the number of recreational fishery POIs within each grid was systematically tallied. Based on POI density, grids were classified into four categories: non-recreational, low-density, medium-density, and high-density, corresponding to the intervals of [0, 1), [1, 10), [10, 50), and ≥50, respectively. As illustrated in Figure 2 (2018), red grids indicate high-density areas, yellow grids represent medium-density areas, green grids denote low-density areas, and white areas mark the absence of recreational fisheries.

The aforementioned framework enabled the quantification of recreational fishery density across the Yangtze River Basin (2015–2024) at 1.875° × 1.25° resolution. To predict recreational fishery density for 2025–2035, a machine learning model was trained on historical density categories and corresponding features at the grid level. Given that the model aimed to predict relative trends in recreational fishery density rather than absolute biophysical conditions, all environmental features—both for the historical period (2015–2024) and the projection period (2025–2035)—were uniformly derived from the CMIP6 dataset (https://esgf-data.dkrz.de/ accessed on 5 June 2025) using the CanESM5 climate model under the SSP2-4.5 scenario. This approach ensured consistency across time spans and provided future-oriented, scenario-based data suitable for machine learning training and prediction. Notably, CMIP6 projection data have been widely used to substitute for observational data even during historical periods, particularly in regions with limited measurement coverage, and have been shown to perform reliably in replicating historical climate trends [35,36]. In addition to climatic factors, this study incorporates elevation (DEM), total population, distance to the coastline, and distance to the nearest wetland derived from WorldPop (https://www.worldpop.org/ accessed on 5 June 2025). It also includes the number of built-up areas, cropland, and wetlands within each grid cell extracted from GLC (http://www.esa-worldcover.org/en/ accessed on 5 June 2025). The feature names and their definitions are provided in

Table 2. Environmental features used for modeling recreational fishery distribution, along with their definitions.

Feature	Descriptions	Feature	Descriptions
C1	Carbon Mass in Coarse Woody Debris (kg C/m2)	BIO1	Annual mean temperature (°C × 10)
C2	Total Carbon in All Terrestrial Carbon Pools (kg C/m2)	BIO2	Mean diurnal range (°C ×1 0)
C3	Carbon Mass in Leaves (g C/m2)	BIO3	Isothermality (%)
C4	Carbon Mass in Litter Pool (g C/m2)	BIO4	Standard deviation of temperature seasonal change (unitless)
C5	Carbon Mass in Products of Land-Use Change (kg C/m2)	BIO5	Max temperature of the warmest month (°C × 10)
C6	Carbon Mass in Roots (g C/m2)	BIO6	Min temperature of the coldest month (°C × 10)
C7	Carbon Mass in Fast Soil Pool (g C/m2)	BIO7	Temperature annual range (°C × 10)
C8	Carbon Mass in Medium Soil Pool (g C/m2)	BIO8	Mean temperature of the wettest quarter (°C × 10)
C9	Carbon Mass in Slow Soil Pool (g C/m2)	BIO9	Mean temperature of the driest quarter (°C × 10)
C10	Carbon Mass in Model Soil Pool (g C/m2)	BIO10	Mean temperature of the warmest quarter (°C × 10)
C11	Carbon Mass in Vegetation (kg C/m2)	BIO11	Mean temperature of the coldest quarter (°C × 10)
C12	Carbon Mass in Wood (kg C/m2)	BIO12	Annual average precipitation (mm)
C13	Total Nitrogen Lost to the Atmosphere (Sum of NHx, NOx, N2O, and N2) (kg N/ha/year)	BIO13	Precipitation of the wettest month (mm)
C14	Total Plant Nitrogen Uptake (Sum of Ammonium and Nitrate) Irrespective of the Source of Nitrogen (kg N/ha/year)	BIO14	Precipitation of the driest month (mm)
C15	Carbon Mass Flux out of Atmosphere Due to Gross Primary Production on Land (kg N/ha/year)	BIO15	Precipitation seasonality (coefficient of variation) (%)
C16	Carbon Mass Flux out of Atmosphere Due to Net Primary Production on Land (g C/m2/year)	BIO16	Precipitation of the wettest quarter (mm)
C17	Carbon Mass Flux into Atmosphere Due to Heterotrophic Respiration on Land (g C/m2/year)	BIO17	Precipitation of the driest quarter (mm)
C18	Daily Maximum Near-Surface Air Temperature (°C)	BIO18	Precipitation of the warmest quarter (mm)
C19	Daily Minimum Near-Surface Air Temperature (°C)	BIO19	Precipitation of the coldest quarter (mm)
C20	Near-Surface Air Temperature (°C)	DEM (Elevation)	Elevation above sea level, indicating terrain height (m)
Population	Total population in each grid cell (count)	Dist_Coast	Distance to the nearest coastline (km)
Cropland_Count	Number of cropland pixels within the grid cell (km2)	Wetland_Count	Number of wetland pixels within the grid cell (km2)
Dist_Wetland	Distance to the nearest wetland (km)	Builtup_Count	Number of built-up pixels within the grid cell (km2)

. All datasets share a native resolution consistent with the aforementioned grids, with latitude–longitude dimensions of 1.875° × 1.25°.

In the context of recreational fisheries, fishery density is largely shaped by two factors: the availability of fishery resources and the intensity of human activity. Although not directly causal, these features were selected based on predictive performance, data availability, and compatibility with future projections, as required in a machine learning context. Specifically, BIO1–BIO19 reflect temperature and precipitation patterns that influence aquatic ecosystem conditions—such as water availability, thermal stability, and seasonality—and are therefore used as ecological proxies for fish biomass and habitat suitability [37]. In contrast, C1–C20 represent terrestrial carbon stocks, fluxes, and nitrogen dynamics, which respond to land-use change, emissions, and vegetation shifts. These features thus serve as indirect proxies for human activity intensity and local socioeconomic conditions [38,39,40], both of which shape the spatial footprint of recreational fisheries.

Notably, the 1.875° × 1.25° resolution was chosen to match the native spatial resolution of the CanESM5 model, thereby ensuring internal consistency and avoiding potential biases from artificial downscaling. Specifically, human activity-related data are typically available at resolutions no finer than approximately 1° × 1°. Even when such data are downscaled, the resulting outputs generally remain at or above this scale, meaning the spatial patterns continue to reflect coarse-level distributions. As a result, finer-resolution modeling based on such inputs may create an illusion of precision without substantially improving spatial accuracy. Furthermore, CanESM5 provides a rich set of climate- and carbon-related variables that are particularly suited to modeling socio-environmental systems. This resolution is consistent with recent CMIP6-based studies of human–environment interactions [41,42].

2.2.2. Data for Flagship Species

The Chinese alligator, the Yangtze finless porpoise, and the scaly-sided merganser were selected as representative flagship species of the Yangtze River Basin. Although flagship species exhibit traits such as endangerment, charisma, or high intelligence, the sole criterion used in this study was whether it had been selected to promote ecological conservation [43]. The following presents evidence supporting the selection of these species to promote ecological conservation: First, the Yangtze finless porpoise has been designated as a flagship species by the Ministry of Agriculture and Rural Affairs of China, with the strategic goal of advancing the conservation of aquatic biological resources. This designation specifically aims to enhance the understanding of fishing restrictions, particularly a 10-year fishing ban, among fishermen in the Yangtze River Basin [44]. Second, news of the rescue of the scaly-sided merganser by fishermen has been repeatedly reported in China to promote the spirit of harmonious coexistence between humans and nature. Finally, since the Chinese alligator became a viral sensation on short-video platforms, national media have consistently reported efforts to correct misconceptions regarding its dietary behavior [45].

Sighting reports for the three flagship species were collected and verified. The corresponding geographic coordinates were also collected to delineate the Yangtze River Basin into unsuitable, low suitability, moderate suitability, and high suitability zones for these three species. These reports were sourced from the citizen science platforms iNaturalist (https://www.inaturalist.org/ accessed on 30 December 2024) and Birdreport (http://www.birdreport.cn/ accessed on 30 December 2024), as well as from former biological studies [46,47]. The locations of the sightings for the three flagship species are shown in Figure 3. The specific coordinates are provided in Supplementary Table S1.

For aquatic organisms, habitat suitability can be influenced by a wide range of environmental factors, such as BIO1–BIO19, water depth, river flow velocity, substrate type, salinity, chlorophyll-a concentration, distance to shoreline, DEM, slope, and aspect. For instance, the habitat suitability of the Yangtze finless porpoise is influenced by various environmental factors, including water depth, substrate type, salinity, flow velocity, prey availability, and other habitat characteristics [48,49,50,51]. Excluding water depth, substrate type, chlorophyll-a concentration and salinity—due to limited data availability in the Yangtze River Basin—all other features mentioned above were evaluated and incorporated into the model to evaluate habitat suitability. BIO1–BIO19 were primarily obtained from the CMIP6 BCC-CSM2-MR climate model under the SSP2-4.5 scenario. River discharge and surface water temperature data were sourced from the ECMWF Global Flood Awareness System (https://data.jrc.ec.europa.eu/collection/id-0069/ accessed on 5 June 2025). The distances to the coastline and inland water bodies, as well as historical population data, were sourced from the WorldPop database (https://hub.worldpop.org/geodata/listing?id=75/ accessed on 5 June 2025), and the future projections were derived from the gridded population dataset developed by Chen et al. [52]. Digital Elevation Model data were obtained from the NASA SRTM mission. The calculation of land cover areas was based on data from the GlobeLand30 database (http://www.esa-worldcover.org/en/ accessed on 5 June 2025). The feature names and their definitions are given in

Table 3. Environmental features used for modeling habitat suitability, along with their definitions.

Feature	Descriptions	Feature	Descriptions
BIO1	Annual mean temperature (°C × 10)	BIO12	Annual average precipitation (mm)
BIO2	Mean diurnal range (°C × 10)	BIO13	Precipitation of the wettest month (mm)
BIO3	Isothermality (%)	BIO14	Precipitation of the driest month (mm)
BIO4	Standard deviation of temperature seasonal change (unitless)	BIO15	Precipitation seasonality (coefficient of variation) (%)
BIO5	Max temperature of the warmest month (°C × 10)	BIO16	Precipitation of the wettest quarter (mm)
BIO6	Min temperature of the coldest month (°C × 10)	BIO17	Precipitation of the driest quarter (mm)
BIO7	Temperature annual range (°C × 10)	BIO18	Precipitation of the warmest quarter (mm)
BIO8	Mean temperature of the wettest quarter (°C × 10)	BIO19	Precipitation of the coldest quarter (mm)
BIO9	Mean temperature of the driest quarter (°C × 10)	river_discharge	Mean river discharge per grid cell, indicating water flow volume (m3/s)
BIO10	Mean temperature of the warmest quarter (°C × 10)	surface_water_temp	Temperature of surface water bodies, reflecting aquatic thermal conditions (°C)
BIO11	Mean temperature of the coldest quarter (°C × 10)	dist_inland	Distance to the inland water body (m)
Slope	Rate of elevation change (°)	Aspect	Direction the slope faces (0–360°), potentially influencing microclimates (°). 0° = north, 90° = east, 180° = south, 270° = west
land_type	Classification of land surface, such as forest, urban, and wetland (none)	DEM	Elevation above sea level, indicating terrain height (m)
dist_coast	Distance to the coastline (m)

.

Notably, among all environmental layers, two variables—flow velocity and surface water temperature—lacked official future projections. Therefore, their future values were independently forecasted using Long Short-Term Memory neural networks, as detailed in the Supplementary Listing S4.

This study downscaled CMIP6 BCC-CSM2-MR climate data from 30 km × 30 km to 1 km × 1 km, enabling the integration of fine-scale variables such as flow velocity and surface water temperature into the species distribution model. Higher spatial resolution has been shown to significantly enhance model performance, particularly for niche-specialist species sensitive to environmental variation [53,54]. It also reduces ecological distortion caused by coarse-scale smoothing and aligns with emerging trends in cross-scale biodiversity modeling for conservation planning [55]. The prediction of recreational fishery distribution in this study adopted a 1.875° × 1.25° resolution primarily for two reasons. First, carbon-related variables—used as proxies for human activity intensity—are only available at coarse spatial scales. Second, human activity patterns are inherently aggregated and diffuse; thus, finer resolutions do not necessarily enhance model accuracy and may instead introduce spatial noise.

Two CMIP6 climate models were employed in this study—CanESM5 for recreational fishery POI prediction and BCC-CSM-MR for flagship species distribution—reflecting the distinct ecological and spatial requirements of each task. Downscaling CanESM5 for ecological modeling would compromise fine-scale heterogeneity critical for species–habitat relationships, while upscaling BCC-CSM-MR would weaken key climate and carbon-cycle signals necessary for human-centered predictions. This task-specific model selection is both theoretically grounded [56] and supported by previous applications in coupled socio-ecological modeling where different subsystems were modeled using different climate inputs [57,58]. By leveraging each model’s strengths within its optimal spatial and thematic domain, this cross-scale integration framework enhances the reliability of subsystem-specific predictions while enabling robust assessments of future interactions between human activity and biodiversity under climate change.

2.3. Methods

2.3.1. Random Forest with Spatial Analysis

This study employed the Random Forest (RF) algorithm to predict the future spatial distribution of recreational fisheries. RF is an ensemble of learning-based machine learning methods that enhances model accuracy by aggregating the outputs of multiple decision trees [59,60]. It performs well in both classification and regression tasks, offering advantages such as the ability to handle multivariate data, capture complex nonlinear feature relationships, and resist overfitting. RF is also compatible with a variety of bioclimatic and carbon-related variables.

In this study, the open-source RFsp project from the ranger package in the R Studio (Version 2023.06.1+524) environment was utilized. By integrating spatial analysis, RFsp effectively addresses spatial autocorrelation and other disturbances, enabling predictions of land parcel types and spatial distributions [61]. The training dataset covers data from 2015 to 2024, and the environmental features for prediction are based on climate scenario data from 2025 to 2034. By predicting the future categories of each 1.875° × 1.25° grid cell, the model estimates the spatial distribution of future recreational fishing POIs.

To enhance model performance and reduce overfitting, we conducted systematic hyperparameter tuning for the Random Forest regression model. A grid search strategy was used to explore key parameters, including the number of trees (n_estimators = 1000), the number of features considered at each split (max_features = 7, 10, 15), minimum samples per leaf (min_samples_leaf = 1, 3, 5), and the split criterion (squared_error or absolute_error). The tuning process was implemented using GridSearchCV with 5-fold cross-validation to ensure stable and unbiased evaluation. The best parameter set was selected based on cross-validated performance and used to retrain the final model for improved predictive accuracy. The final model was evaluated using regression metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). Moran’s I statistic was also used to check spatial autocorrelation in the residuals. The predictions on future data were consistent and reliable. All preprocessing steps and code are included in the Supplementary Listings S1–S3.

2.3.2. Maximum Entropy Models

The Maximum Entropy Model (MaxEnt) is a statistical learning method based on information theory [62]. MaxEnt estimates species distribution by integrating known occurrence data (latitude and longitude) with environmental features and identifies areas where a species is likely to occur [63]. The model operates on the principle that the most accurate probability distribution is the one that maximizes entropy [64]. An advantage of MaxEnt is its ability to rely solely on presence data, addressing the common challenge of missing absence data in species distribution studies [65]. It also performs well even when sighting records may be biased by uneven human activity, because the model compares occurrence points with a large number of background points sampled across the entire study area. This helps reduce the influence of observer density on prediction. Additionally, it accommodates both continuous and categorical data and considers feature interactions to improve predictive accuracy. Therefore, because sighting reports represented only small portions of the ranges of the Chinese alligator, the Yangtze finless porpoise, and the scaly-sided merganser, MaxEnt was used to predict the spatial distributions of the species.

In the current species distribution modeling, 80% of the sample points and the multi-year average of environmental features from 2015 to 2024 were used as training data, while the remaining 20% of the sample points were reserved for validation. Based on this model, the predicted average environmental values for 2025 to 2035 were then applied to simulate future distributions, enabling spatial prediction of potential habitats for the flagship species. For certain datasets where future projections were not available from existing data services, this study conducted independent predictions; the derivation process for these forecasts is detailed in the Appendix A Figure A1, Figure A2 and Figure A3.

All variables listed in Table 3 were subjected to preprocessing checks before being included in the MaxEnt model. To enhance model stability and predictive performance, Pearson correlation analysis was first conducted, and environmental variables with correlation coefficients less than 0.8 relative to species occurrence records were excluded. This ensured that only features with strong statistical relevance to species distribution were retained. Subsequently, variance inflation factor (VIF) analysis was performed to identify multicollinearity. Variables with VIF values greater than 10 were removed to reduce information redundancy and modeling errors, ensuring that only statistically independent variables were used for MaxEnt model training.

The model was executed using MaxEnt’s replicate mode (replicates = 10), meaning the model was trained ten times on different data subsets, and the average prediction result was used to enhance robustness. The maximum number of iterations per run was set to 500 to allow sufficient model convergence without overfitting. The final output included the mean habitat suitability map and standard deviation evaluation to indicate areas of uncertainty. The complete modeling workflow is illustrated in Figure 4. It is important to note that this study does not aim to analyze which specific environmental factors influence the distribution of the three flagship species. Therefore, the ROC curves, the Occam’s razor test, and the analysis titled ‘Relationship Between the Distribution of Flagship Species and Environmental Features’ are all included in the Appendix A Figure A5, Figure A6, Figure A7, Figure A8, Figure A9, Figure A10, Figure A11 and Figure A12.

3. Result

3.1. Spatial Distribution and Future Projection of Recreation Fisheries in the Yangtze River Basin

As shown in Table 1, the number of recreational fishery POIs in the Yangtze River Basin has exhibited a consistent upward trajectory, increasing from 340 in 2015 to 10,777 in 2024. This trend is further visualized in Figure 5. Grids (1.875° E × 1.25° N) were classified by POI density as non-recreational [0, 1), low [1, 10), medium [10, 50), and high ≥ 50; red, yellow, green, and white indicate these levels, respectively. In 2015, most grid cells contained no or very few POIs, as indicated by the dominance of white and green cells. Over time, more grids turned yellow and red, especially in the central and eastern regions of the basin. By 2024, a substantial portion of the grids had turned red, which highlighted the significant expansion of recreational fishery activities. This distribution was also influenced by abundant fishery cultural resources, diverse natural landscapes, and advancements in economic and social development [66,67].

This study utilized the Rfsp algorithm in conjunction to predict the density types of recreational fishing POIs across 154 grid cells in the Yangtze River Basin for the years 2025–2035. A total of 42 environmental factors were selected as predictive features. The dataset was split into 80% for training and 20% for testing. To prevent overfitting, the Random Forest model was evaluated using training/testing R² comparison, k-fold cross-validation, and Moran’s I residual analysis. Model performance indicators suggest strong generalization capacity and no signs of overfitting. Specifically, the model achieved an R² of 0.973 on the training set and 0.860 on the test set, indicating a reasonable performance gap. Cross-validation yielded a mean R² of 0.774 (±0.029), suggesting stable model behavior across data folds. The test set also showed a relatively low Mean Squared Error (MSE) of 1308.97. Furthermore, the Moran’s I statistic for model residuals was −0.104, indicating that the residuals were spatially random and exhibited no significant clustering or spatial bias. Collectively, these results provide strong evidence that the model is robust, well-generalized, and not affected by overfitting or spatial autocorrelation in the residuals.

The prediction maps (Figure 6) show projected recreational fishery patterns (2025–2035) based on 1.875° E × 1.25° N grids, all in eastern longitude and northern latitude. Grids are classified by POI density as non-recreational [0, 1), low [1, 10), medium [10, 50), and high ≥50, shown in white, green, yellow, and red. The forecasts show steady expansion, especially toward the northern and southwestern regions. Medium-density areas are expected to grow most consistently, while high-density areas are projected to peak in 2024 and then gradually contract as many transition into medium density, indicating spatial redistribution rather than sharp decline. Although the total number of POIs is projected to decrease from 10,777 in 2024 to 7357 in 2035, medium- and low-density areas continue expanding, reflecting a shift toward broader but more moderate recreational activity. Compared to observed distributions from 2015 to 2024 (Figure 5), the projected patterns suggest both continuity and spatial restructuring. To clarify the locations of projected high-density areas, Appendix A 

Table A1. Counties or cities hosting the centers of high-density recreational fisheries in 2024 and 2035.

	County Level	Prefecture-Level City Level
Year 2024	Longli County, Nanchang County, Santai County, Xinye County, Changshou District, Jinyang County, Ziyang District, Wuzhong District, Xiushui County, Susong County, Yingshan County, Anfu County, Suichuan County, Lichuan City, Yunyang County, Huangpi District, Yuanling County, Jiangling County, Tongzi County, Yiliang County, Shimen County, Yucheng District, Bozhou District, Xingshan County, Mingguang City, Xiangtan County, Wenchuan County, Liuba County, Nanjiang County, Zhongxiang City, Jiang’an County, Anyue County, Xian’an District, Jiujiang District	Chuxiong Yi Autonomous Prefecture, Jingmen City, Yibin City, Ya’an City, Qiannan Buyi and Miao Autonomous Prefecture, Changde City, Zunyi City, Nanchang City, Kunming City, Enshi Tujia and Miao Autonomous Prefecture, Yichang City, Jiujiang City, Anqing City, Xiangtan City, Nanchong City, Hangzhou City, Jingzhou City, Chuzhou City, Nanyang City, Wuhan City, Hanzhong City, Ji’an City, Xianning City, Aba Tibetan and Qiang Autonomous Prefecture, Ziyang City, Taizhou City, Yiyang City, Wuhu City, Bazhong City, Lu’an City, Mianyang City, Suzhou City
Year 2035	Longli County, Nanchang County, Santai County, Xinye County, Changshou District, Ningshan County, Ziyang District, Wuzhong District, Xiushui County, Susong County, Huangpi District, Yuanling County, Jiangling County, Tongzi County, Yiliang County, Shimen County, Yucheng District, Bozhou District, Xingshan County, Mingguang City, Xiangtan County, Wenchuan County, Zhongxiang City, Jiang’an County, Anyue County, Xian’an District, Jiujiang District	Jingmen City, Yibin City, Quanzhou City, Ya’an City, Qiannan Buyi and Miao Autonomous Prefecture, Changde City, Zunyi City, Nanchang City, Kunming City, Zhumadian City, Yichang City, Jiujiang City, Anqing City, Xiangtan City, Hangzhou City, Jingzhou City, Chuzhou City, Nanyang City, Wuhan City, Xianning City, Shangqiu City, Aba Tibetan and Qiang Autonomous Prefecture, Ziyang City, Taizhou City, Yiyang City, Wuhu City, Lu’an City, Mianyang City, Wenzhou City, Suzhou City, Fuyang City

lists the counties and prefecture-level cities where the centers of high-density grids are located in 2024 and 2035.

3.2. Spatial Distribution and Future Projection of Three Flagship Species in the Yangtze River Basin

Following Pearson correlation analysis and multicollinearity testing (see Appendix A Figure A2 and Figure A3), key environmental features selected for species distribution modeling were DEM, BIO1, BIO2, BIO4, BIO15, dist_coast, dist_inland, land_type, river_discharge, slope, and surface_water_temp for the Chinese alligator; DEM, BIO15, BIO7, BIO4, BIO13, BIO14, dist_coast, dist_inland, land_type, river_discharge, and aspect for the Yangtze finless porpoise; and DEM, BIO1, BIO2, BIO4, BIO14, BIO15, BIO16, dist_coast, dist_inland, land_type, river_discharge, slope, surface_water_temp, and aspect for the scaly-sided merganser.

The MaxEnt model outputs habitat suitability as a probability value ranging from 0 to 1, which this study classified into four suitability zones: unsuitable (0–0.2), low (0.2–ha0.4), medium (0.4–0.6), and high (≥0.6).

3.2.1. Spatial Distribution and Future Projection of the Chinese Alligator

After running MaxEnt 10 times, the mean AUC reached 0.994, demonstrating strong model performance in the analysis of the Chinese alligator’s spatial distribution and future projections. Figure 7 presents the comparative distribution of suitable habitats for the Chinese alligator under current and future conditions, based on the Albers coordinate system. The low-suitability, medium-suitability, and high-suitability zones are represented by pale yellow, orange, and red, respectively.

The habitat suitability map for the Yangtze alligator shows that its potential distribution is primarily concentrated in the lower Yangtze River Basin, particularly in the eastern region. Red areas are mainly in Anhui and Zhejiang provinces, with the orange and pale yellow zones extending outward. In contrast, most of the basin is classified as white, which indicates that only a small portion offers favorable conditions for the species. Notably, the red zones encompass the core wild population area in southeastern Anhui, especially around Xuancheng, as well as the Chinese Alligator Provincial Nature Reserve in Huzhou, Zhejiang, where the population is mainly captive-bred. Compared with the current distribution, future projections suggest that the red zones will continue to be concentrated in Anhui and Zhejiang, while the orange areas—particularly around Wuxi—are expected to shrink. However, this does not necessarily indicate a decline in population size; ongoing conservation efforts are likely to support population recovery despite localized reductions in suitable habitat [68].

3.2.2. Spatial Distribution and Future Projection of the Yangtze Finless Porpoise

The MaxEnt model (mean AUC = 0.987) demonstrated good predictive performance. Figure 8 shows the current and future habitat suitability distribution of the Yangtze finless porpoise, using the Albers coordinate system and the same three-zone classification.

The suitable habitats for the Yangtze finless porpoise cover different seasonal ranges. During the dry season, porpoises mainly concentrate in the main channels of the middle and lower Yangtze River, especially in the sections of Anhui, Hubei, and Jiangsu. In the wet season, they are often active in Poyang Lake, Dongting Lake, their connected tributaries, and parts of the Gan River. Figure 8 illustrates the change in habitat suitability for the Yangtze finless porpoise, comparing the current distribution (left) to future projections (right). At present, high-suitability habitats are concentrated along the middle reaches of the Yangtze River, forming a relatively continuous ecological corridor that spans from the Three Gorges Reservoir area through central Hubei to the lower reaches in Jiangxi and Anhui Provinces. Key complementary habitats are also located around major connected water bodies such as Poyang Lake and the Yangtze estuary. In the future scenario, while core zones such as Poyang Lake and parts of central Hubei remain suitable, the overall spatial extent and connectivity of suitable habitats are projected to decline. High-suitability areas in western Hubei and eastern Anhui contract notably, and the previously continuous distribution becomes more fragmented. No new high-suitability regions emerge, suggesting increasing ecological degradation under anticipated environmental pressures.

3.2.3. Spatial Distribution and Future Projection of the Scaly-Sided Merganser

The MaxEnt model (mean AUC = 0.902) suggested reasonable performance in predicting the spatial distribution of the scaly-sided merganser.

Figure 9 shows a spatial comparison between the current and projected future distribution of highly suitable habitats for the scaly-sided merganser in the Yangtze River Basin. At present (left panel), high-suitability areas are concentrated in the western Sichuan Basin, the southern Qinling Mountains, the Poyang Lake region, the Dongting Lake water network, the Jingjiang section of the Yangtze River, the Taihu Lake area, and the Yangtze River estuary (Shanghai and eastern Jiangsu). In the future (right panel), these core areas remain highly suitable, but notable expansions occur. High-suitability zones extend northeastward into the Sichuan Basin and westward around the Dongting Lake region. There is also increased habitat connectivity between fragmented areas, especially in the central and eastern basin. Overall, the future projection indicates both an expansion and consolidation of suitable habitats, suggesting improved landscape conditions or shifting climatic suitability.

3.3. Overlap Area Between Recreational Fisheries and Three Flagship Species

This study analyzed the spatial interactions between recreational fisheries and flagship species by measuring the area and changes in two coupling scenarios: (a) high-density fisheries with high-suitability habitat (high–high) and (b) medium-density fisheries with high-suitability habitat or high-density fisheries with moderate-suitability habitat (medium–high or high–medium, respectively).

Table 4. Annual area (km²) of overlapping zones between recreational fisheries and flagship species habitat suitability (2015–2035).

Year	Name of Flagship Species
	Chinese Alligator		Yangtze Finless Porpoise		Scaly-Sided Merganser
	Med–High	High–High	Med–High	High–High	Med–High	High–High
2015	8624	0	11,700	368	23,581	0
2016	5910	5139	13,033	368	44,320	5303
2017	4543	8624	16,200	5033	56,834	7506
2018	4543	8624	16,767	5956	71,451	15,946
2019	4943	8819	17,908	7493	116,279	59,436
2020	4973	8819	15,385	13,237	120,880	63,572
2021	4973	8819	15,428	13,256	114,352	79,606
2022	4973	8819	15,429	13,256	114,363	79,606
2023	4973	8819	15,429	13,256	116,860	81,568
2024	4973	8819	15,429	13,256	115,826	85,359
2025	5820	8557	12,116	15,636	150,021	106,850
2026	5820	8557	12,176	15,638	153,379	112,947
2027	5820	8557	12,113	15,636	154,455	100,425
2028	5820	8557	12,116	15,636	154,505	100,419
2029	5820	8557	12,113	15,636	149,937	106,850
2030	5820	8557	12,116	15,636	154,738	98,845
2031	5820	8557	12,116	15,636	148,931	105,753
2032	5820	8557	12,116	15,636	151,493	109,091
2033	5820	8557	12,116	15,636	150,993	104,153
2034	5820	8557	12,116	15,636	148,879	104,153
2035	5820	8557	12,179	15,638	149,989	112,730

illustrates a consistent upward trend in both high–high and medium–high overlap zones between 2015 and 2024, with projections indicating continued expansion through 2035. This pattern reflects the growing spatial convergence between recreational fishing intensity and flagship species habitat suitability in the Yangtze River Basin. Among the three species, the scaly-sided merganser exhibits the most extensive and dynamic spatial overlap, followed by the Yangtze finless porpoise, while the Chinese alligator maintains the smallest and most stable overlap area. These interspecies differences underscore the varying degrees of exposure to anthropogenic pressures and highlight the urgency of species-specific conservation planning. Projections to 2035 suggest that the Chinese alligator’s overlap zone will likely remain stable, indicating a relatively fixed spatial interaction. In contrast, the Yangtze finless porpoise is expected to experience continued spatial intensification. The scaly-sided merganser is projected to undergo the greatest temporal variation in overlap area, signaling increased ecological sensitivity to changes in human activity distribution.

Figure 10, Figure 11 and Figure 12 compare the current (2024) and projected (2035) spatial overlaps between recreational fisheries and the habitats of the three flagship species. Detailed classification of overlap intensities and color coding is provided in the figure captions.

Figure 10 presents a comparison of the overlap regions (left: current, right: predicted). On the left side, the current overlap region (deep red) is primarily located at the junction of southern Anhui, southwestern Jiangsu, and northern Zhejiang. This region features diverse topography, including mountainous areas in southern Anhui, hills in southwestern Jiangsu, and plains in northern Zhejiang, with an extensive water system comprising wetlands, lakes, and rivers. This area is governed by a subtropical monsoon climate, characterized by mild temperatures, abundant precipitation, and infrequent extreme weather events. Notably, this region experiences fewer instances of extreme low temperatures in winter, making it a highly suitable habitat for the Yangtze alligator. In addition, the region benefits from a well-developed transportation network and sustains a diverse industrial structure, including agriculture, aquaculture, tourism, and ecological economies. This region constitutes a critical interface between the core habitat of the Chinese alligator and areas of intensive recreational fishery activities, posing challenges to balancing biodiversity conservation with economic development.

On the right side, the predicted overlap region (deep red) indicates a significant expansion, primarily in the water network region along the southern bank of the Yangtze River, west of Taihu Lake, and east of Chaohu Lake. This expansion covers administrative regions such as Ma’anshan, Wuhu, and Xuancheng, as well as wetland areas like the Yuxi River network, Shijiu Lake, and Gucheng Lake. The model predicts an increase in recreational fishing POI density in this region, which is expected to drive a transition from medium to high-density recreational fishing activities. This prediction is partially supported by the strategic plan of the Anhui Provincial Department of Agriculture and Rural Affairs, which designates these areas for development into Urban Recreational Fisheries Experience Zones.

Figure 11 compares the current and predicted overlap areas between the suitable habitat of the Yangtze finless porpoise and high-density recreational fishery zones. On the left side, the current coupling areas (deep red) include six key regions: (a) the Poyang Lake water network (Northern and Eastern Jiangxi), (b) the Yangtze River coastal transport region (Anhui, Jiangsu, Jiangxi), (c) the Jing River section of the Yangtze River (Southern Hubei), (d) the Taihu Lake and canal water town region (Southern Jiangsu, Northern Zhejiang), (e) the Yangtze River Delta estuary (Eastern Jiangsu), and (f) the Western Anhui hilly and mountain stream region (Western Anhui, Eastern Hubei). These areas have subtropical monsoon climates with moderate summer temperatures and insignificant diurnal variations, which create favorable conditions for the Yangtze finless porpoise. The six regions are characterized by thriving fisheries, aquaculture, and tourism-driven economies underpinned by rich water resources and active trade. These factors have contributed to recreational fishing becoming a highly developed industry, increasing the likelihood of encounters with the Yangtze finless porpoise.

On the right side, the predicted expansion of the coupling areas (deep red) is mainly along the Anqing–Chizhou–Tongling–Wuhu corridor in the Wanjiang region. Although the Yangtze River Protection Law theoretically prohibits all fishing in the mainstream until 2030, it is allowed in non-mainstream waters. In practice, however, enforcing a strict one-person-one-rod policy remains a challenge.

Figure 12 shows that the suitable habitats of the scaly-sided merganser and high-density recreational fisheries were primarily distributed in the following regions: (a) the Tiaoxi River Basin in the southwestern Taihu Lake region, (b) the lower Yangtze River Delta west of Chongming Island, (c) the Gan River Basin south of Poyang Lake, (d) the Jing River and the southern edge of the Jianghan Plain, (e) the Xiang River Basin south of Dongting Lake, and (f) the upper reaches of the Minjiang-Tuojiang Rivers in the western Sichuan Basin. These regions are characterized by relatively low elevations with gently undulating terrain and an optimal mean temperature during the wettest quarter, creating favorable conditions for nesting and breeding of the scaly-sided merganser. The rich water resources and abundant fish populations in these areas not only provide a stable food source but also sustain the development of recreational fisheries.

Identifying high–high areas does not necessitate fortress-like conservation measures that entirely exclude angling. Under Chinese law, angling is prohibited year-round in nature reserves, aquatic germplasm conservation zones, and key habitats within the Yangtze River Basin. Additionally, a seasonal fishing moratorium is enforced across most of the basin from March to June to protect fish during critical breeding periods. However, regardless of legal provisions, the spatial and temporal overlap between recreational fishing and wide-ranging flagship species, such as charismatic migratory birds, appears inevitable. Moreover, in economically dense regions—such as the area spanning western Taihu Lake to eastern Chaohu Lake that covers the border zones of Zhejiang, Jiangsu, Shanghai, and Anhui—the continuous costs of actively managing protected areas to isolate human tourism and recreation from wildlife are prohibitively high. However, global practices indicate that allowing controlled access to wildlife habitats for tourism can effectively generate operational funding for local ecosystem conservation. When managed effectively, the public appeal of flagship species can serve as a key attraction for recreational fisheries. In this way, the fisheries provide ecosystem services to both residents and non-local visitors, enhance environmental awareness, and help to secure financial resources for local conservation efforts. Therefore, high–high areas present both opportunities and challenges, with the potential for successful integration of conservation and development. However, they may also face serious issues in the future, especially if species with high public visibility encounter threats in these areas, which could potentially lead to substantial public scrutiny and intense societal pressure.

4. Discussion

This study reveals recreational fisheries in the Yangtze River Basin have experienced rapid expansion and spatial clustering from 2015 to 2024, which is consistent with the trends identified in existing research [62,66], and the reasons for this trend, as proposed in this study, are attributed to a combination of socio-economic, environmental, and policy-driven factors. Urbanization, the growth of ecotourism, and increasing demand for leisure activities have fueled the proliferation of recreational fishing, especially in the middle and lower reaches of the Yangtze River, such as Jiangsu, Anhui, and Jiangxi provinces. The national “Ten-Year Fishing Ban” (2019–2029), which banned productive fishing in the mainstem and key tributaries of the Yangtze River, has accelerated this transition by displacing professional fishers, many of whom have shifted to recreational fishing support services, including food, lodging, and rescue services. Furthermore, substantial government investment—exemplified by Jiangsu Province’s annual allocation of over RMB 3 million and a cumulative RMB 200 million in infrastructure—has promoted recreational fisheries as a strategic industry during the 14th Five-Year Plan. These financial incentives and supportive policies, coupled with favorable natural conditions, have contributed to the spatial concentration of high-density fishing activities.

This study demonstrated a steadily increasing spatial overlap, both now and in the foreseeable future, between high-density recreational fishing zones and the core habitats of flagship species. The spatial overlap reflects a convergence of species-specific environmental preferences and anthropogenic patterns—a pattern further supported by this study’s habitat suitability analyses, which are consistent with biological insights from existing research. The Chinese alligator favors low-elevation hilly wetlands in southern Anhui and Zhejiang [69,70], areas that also provide accessible, shallow, and calm waters attractive to amateur anglers. The Yangtze finless porpoise, reliant on large, slow-flowing water bodies with stable thermal conditions [71,72], is concentrated in lake-delta systems like Poyang and Dongting—locations long integrated into cultural fishing traditions and modern tourism infrastructure. The scaly-sided merganser inhabits mountainous river networks [73], particularly those with rich aquatic habitats during the wet season [74], which coincide with scenic river segments that attract seasonal recreational activities. As recreational fishing continues to expand spatially, it increasingly penetrates these ecologically suitable zones—not because they are targeted deliberately, but because human recreational preferences tend to gravitate toward areas with good water quality, scenic beauty, and biodiversity, all of which are also habitat requirements for these flagship species. This convergence explains the observed intensification of spatial coupling from 2015 to 2024, particularly in the Taihu–Chaohu corridor, the Poyang Lake basin, and the Ma’anshan–Wuhu–Xuancheng axis. These regions have transitioned from low- or moderate-overlap areas to high–high coupling hotspots.

The intensification of spatial coupling between recreational fishing and flagship species habitats has not only ecological implications but also growing social and political impacts. As highly recognizable and charismatic species, the Chinese alligator, Yangtze finless porpoise, and scaly-sided merganser carry strong public symbolism and conservation appeal. Their presence near human recreational spaces often increases the likelihood of direct interaction—whether intentional (e.g., wildlife feeding) or incidental (e.g., boat disturbance, noise). While such interactions may generate public interest and even facilitate conservation efforts, they also heighten the risk of behavioral disturbance, dependency on artificial feeding, and increased vulnerability to injury [75].

Moreover, when flagship species are harmed—whether by entanglement, habitat degradation, or human negligence—these incidents are more likely to attract media attention and public outrage [76]. In such cases, public discourse can quickly shift toward demands for strict, exclusionary conservation measures, such as no-access buffer zones or blanket bans on fishing in critical habitats. While well-intentioned, these measures may lead to a “second displacement” effect on local communities, particularly former fishers who have already transitioned to legal, regulated recreational service roles. As such, without inclusive governance frameworks, rising public scrutiny could unintentionally undermine the long-term social viability of recreational fisheries and erode trust between communities and conservation authorities [77,78]. While existing literature offers valuable insights through case-based and longitudinal approaches [79,80,81], there is still a notable gap in spatially explicit analyses that quantify the locations and intensity of conflicts between flagship species and human activities, especially within the domain of recreational or small-scale fisheries. Accordingly, this study takes the Yangtze River Basin as a case to investigate the spatial coupling between highly suitable habitats of flagship species and high-density recreational fishing zones.

This study advances human–nature interaction research by not only identifying current hotspots of spatial overlap between flagship species and recreational fisheries but also projecting their future evolution under climate and socio-economic scenarios, thus revealing where and to what extent ecological–social conflicts may intensify. This study develops an analytical framework to identify potential conflict zones by integrating the MaxEnt model with the Rfsp model. In comparison, while some existing research has utilized the MaxEnt model to explore the spatial distributions of the Chinese alligator, the Yangtze finless porpoise [82], and the scaly-sided merganser [83], relatively few have incorporated predictive modeling to project future distribution patterns. Furthermore, existing studies that apply the MaxEnt model for distribution forecasting are often limited by workload and data availability; most rely on long-term historical averages [84,85,86], particularly the BIO1–BIO19 variables from the WorldClim dataset (1970–2000). This study integrates multiple environmental data sources and projection models to reconstruct more temporally relevant datasets, incorporating both present features (2015–2024) and near-future projections (2025–2035). This allows for a more comprehensive set of environmental predictors and enhances the temporal alignment between species distribution and human activity trends. On the other hand, most existing studies have primarily examined the driving factors behind the spatiotemporal distribution of recreational fisheries [87], while few have attempted to predict their future distribution patterns. This study develops an Rfsp model with relatively low input requirements that effectively predicts the density of recreational fishing POIs across the Yangtze River Basin. Given its generalizability and minimal data demands, the model holds strong potential for application in other regions and offers a practical decision-support tool for environmental authorities seeking to anticipate and manage human–nature interactions.

Importantly, the projection period is deliberately limited to 2025–2035 to ensure model precision and data stability. Both CanESM5 and BCC-CSM2-MR have demonstrated reliable performance in simulating East Asian temperature and precipitation under the SSP245 scenario, particularly for short-to-mid-term projections. Studies have shown that decadal forecasts maintain low uncertainty and close alignment with observed trends [88,89]. By contrast, longer-term projections tend to amplify structural uncertainties and may also be influenced by large-scale industrial policy shifts, thereby reducing the operational feasibility and policy relevance of the results for real-world ecological management. Furthermore, the 10-year projection period aligns with China’s 15th and 16th Five-Year Plans, enhancing the policy relevance and feasibility of the findings, particularly given the government’s generally supportive stance toward recreational fisheries during this period.

Nonetheless, this study has several limitations. First, recreational fisheries were represented using POIs, which may exclude informal or illegal fishing activities and lead to an underestimation of true fishing intensity. Second, species distribution modeling relied on citizen science data, which may introduce spatial biases due to uneven population density, tourism activity, and accessibility. Third, while the study focused on mapping present-day spatial coupling between flagship species and recreational fisheries, it did not explore effective interventions for angler behavior. Finally, due to limitations in data availability, some environmental variables that may significantly influence the distribution of flagship species were not included in the MaxEnt model, which may have affected the accuracy of the predictions to some extent. In fact, when relevant features were insufficiently included in an earlier stage of this study, the predicted suitable areas for flagship species were noticeably larger compared to results using the current set of features. This highlights the ongoing urgency to develop high-resolution environmental datasets that are closely related to habitat suitability for these species. Nonetheless, it is worth emphasizing that the predicted species distribution in this study closely aligns with existing field observations, indicating that the model maintains strong reliability in practical applications.

Future research should prioritize three areas. First, combining model predictions with results from ecological surveys and baseline assessments could help mitigate sampling bias. Second, dynamic, field-based monitoring—such as remote sensing, automated surveillance, and participatory tracking—could better capture the evolving impacts of recreational fisheries. Third, behavioral research using interviews or surveys with anglers is essential to understand their decision-making processes and to design targeted, evidence-based interventions.

5. Conclusions

This study combined spatial modeling of recreational fishing hotspots with verified occurrence records of three flagship aquatic species—the Chinese alligator, Yangtze finless porpoise, and scaly-sided merganser—to examine spatial overlap in the Yangtze River Basin. Results show a rapid expansion of recreational fisheries, particularly in economically developed areas, increasingly overlapping with suitable habitats for these species. Although their ecological preferences vary, all face mounting pressure as fishing activity encroaches on ecologically sensitive areas. This overlap is intensifying over time, especially in interconnected lake and river systems where critical habitats and recreational use coincide.

To address these emerging conflicts, we propose a three-tiered policy framework:

• Adaptive Spatial Planning: Establish dynamic zoning that adjusts fishing intensity based on real-time species distribution and seasonal patterns. For example, seasonal closures during the scaly-sided merganser’s breeding period (March–June) in western Sichuan can align with existing moratoriums. Use early warning systems based on our modeling to anticipate conflict hotspots. Expand the “grid officer” system by integrating ecological monitoring into local governance, enabling grassroots officials to oversee species and habitat conditions alongside routine duties.
• Targeted Education Programs: Design targeted education programs that address common behavioral risks associated with recreational fishing in flagship species habitats, such as feeding Yangtze finless porpoises, disturbing scaly-sided merganser nests, and misconceptions about Chinese alligator behavior. Develop certification programs for recreational fishing operators that demonstrate knowledge of flagship species ecology and responsible interaction protocols. Leverage the viral potential of flagship species on social media to promote evidence-based conservation messaging rather than sensationalized content.
• Innovative Financing Mechanisms: Introduce ecological permit fees for recreational fishing in sensitive zones, with tiered pricing based on proximity to core habitats. Create public–private partnerships that reinvest a share of recreational revenue into habitat restoration, following ecotourism models. Design payment for ecosystem services (PES) schemes to reward local communities for maintaining key habitats.

This study makes several important contributions to the understanding of human–wildlife interactions in aquatic ecosystems:

• Methodologically, we integrated fine-scale species distribution models (1 km resolution) with recreational use data, incorporating both present and projected scenarios while validating citizen science records to reduce sampling bias.
• Practically, we identify spatially explicit high-risk zones, such as the Anqing–Chizhou–Wuhu corridor, to guide targeted enforcement of existing policies like the one-rod rule.
• Conceptually, we highlight that aquatic flagship species require conservation strategies beyond traditional protected areas due to the linear and interconnected nature of their habitats, advocating for network-based governance.

To further enhance our understanding and inform effective conservation efforts, several key research directions are proposed:

• Building on this foundation, critical research needs that will help advance the field in both theoretical and practical dimensions include: Behavioral Studies: Investigating how recreational fishing activities directly impact flagship species behavior and physiology, particularly stress responses in Yangtze finless porpoises and nesting success of scaly-sided mergansers.
• Cumulative Impact Assessment: Developing frameworks to evaluate the combined effects of climate change, fishing pressure, and other anthropogenic stressors on flagship species population viability.
• Governance Effectiveness: Comparative studies of different management regimes (e.g., voluntary guidelines vs. regulated closures) in high-overlap zones to identify optimal approaches.
• Community Perspectives: Ethnographic research on local perceptions of flagship species conservation to improve intervention design.

The Yangtze River Basin presents a microcosm of global challenges in balancing aquatic biodiversity conservation with sustainable resource use. Our study demonstrates that the rapid expansion of recreational fisheries—while economically beneficial—poses significant risks to flagship species through habitat overlap and behavioral disturbance. However, these challenges also present opportunities to develop innovative coexistence models that harness the economic value of recreational fisheries to fund conservation.

The policy framework proposed here emphasizes proactive, science-based management that recognizes both the ecological needs of flagship species and the socio-economic realities of local communities. By implementing adaptive zoning, targeted education, and innovative financing mechanisms, it is possible to transform areas of conflict into models of sustainable coexistence.

Ultimately, the conservation of these flagship species will serve as both a practical achievement and a symbolic indicator of our ability to reconcile human development with ecological integrity in one of the world’s most important freshwater ecosystems. As the Yangtze continues to evolve under climate change and economic transformation, the approaches developed in this study will become increasingly relevant for river systems worldwide facing similar challenges.