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Article

National-Scale Assessment of Climate Change Impacts on Two Native Freshwater Fish Using a Habitat Suitability Model

by
Taeyong Shim
1,
Zhonghyun Kim
1,
Dongil Seo
2 and
Jinho Jung
1,*
1
Division of Environmental Science & Ecological Engineering, Korea University, Seoul 02841, Korea
2
Department of Environmental Engineering, Chungnam National University, Daejeon 34134, Korea
*
Author to whom correspondence should be addressed.
Water 2022, 14(11), 1825; https://doi.org/10.3390/w14111825
Submission received: 10 April 2022 / Revised: 30 May 2022 / Accepted: 1 June 2022 / Published: 6 June 2022
(This article belongs to the Section Biodiversity and Functionality of Aquatic Ecosystems)
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Abstract

:
Climate change, which has the potential to alter water flow and temperature in aquatic environments, can influence the freshwater fish habitat. This study used an ecological habitat suitability model (EHSM), which integrates hydraulic (water depth and velocity) and physiologic (water temperature) suitability, to investigate the impact of climate change on two native freshwater fish species (Zacco platypus and Nipponocypris koreanus) in South Korea. The model predicted that in 2080 (2076–2085), the decrease in average ecological habitat suitability (EHS) will be higher for N. koreanus (19.2%) than for Z. platypus (9.87%) under the representative concentration pathway (RCP) 8.5 scenario. Under the same condition, EHS for Z. platypus and N. koreanus at 36.5% and 44.4% of 115 sites, respectively, were expected to degrade significantly (p < 0.05). However, the habitat degradation for Z. platypus and N. koreanus was much lower (7.8% and 10.4%, respectively) under the RCP 4.5 scenario, suggesting a preventive measure for carbon dioxide emission. Partial correlation analysis indicated that the number of hot days (i.e., days on which the temperature exceeds the heat stress threshold) is the variable most significantly (p < 0.05) related to EHS changes for both species. This study suggests that the EHSM incorporating the effect of water temperature on the growth and heat stress of fish can be a promising model for the assessment of climate change impacts on habitat suitability for freshwater fish.

1. Introduction

Freshwater environments are expected to experience alterations in the flow and thermal regimes of streams and rivers owing to climate change [1,2]. Given that freshwater ecosystems are dependent on the flow and thermal regimes [3,4,5], changes in these are expected to affect the growth, migration, spawning, survival, and distribution of freshwater fish [6,7,8]. Specifically, environmental variables such as water depth, velocity, and temperature are important to freshwater fish habitats [9,10]. Therefore, the effects of climate change on the habitat of freshwater fish should be evaluated for future risk assessment.
Habitat suitability models have been developed to predict potential suitable habitats for freshwater fish, generally using relationships between fish abundance or distribution and environmental variables [11,12,13]. One such mechanistic approach is the habitat suitability index modeling based on the physical habitat simulation system (PHABSIM) [14], which has been widely used to evaluate the impacts of altered hydraulic conditions (depth, velocity, and substrate) on fish habitat [14,15,16,17,18,19,20]. However, habitat suitability models based on the PHABSIM have been limited to altered hydraulic suitability in the assessment of climate change’s impact on fish habitat [21,22,23].
Recently, studies have begun to evaluate the thermal suitability of fish habitats [24,25,26,27], because the effects of global warming on rivers and streams have become inevitable [28,29,30,31]. Recent studies have demonstrated that water temperature is the main factor affecting habitat suitability [25,26,27]. For instance, Morid et al. [25] reported that the effects of climate change on Zacco platypus (pale chub) and Z. temminickii (dark chub) habitat suitability were underestimated when water temperature was not considered in the prediction. In addition, Muñoz–Mas et al. [26] predicted that the suitable spawning habitat of Salmo trutta (brown trout) would decrease owing to an increase in water temperature despite the improvement in hydraulic conditions. Furthermore, Zhang et al. [27] found that, when compared with hydraulic factors such as water depth and velocity, water temperature was the most significant environmental factor affecting the habitat suitability for Coreius guichenoti (largemouth bronze gudgeon) under climate change and dam operation scenarios. However, these habitat suitability models have limitations when evaluating the effect of water temperature on the physiological responses, such as growth and stress, of fish [32].
An ecological habitat suitability model (EHSM) integrating the hydraulic (water depth and velocity) and physiologic (water temperature) suitabilities was developed in a previous study [33]. The EHSM model is a promising tool to predict the effects of climate change on freshwater fish because it was designed to evaluate the effect of water temperature on fish growth and stress [34]. The present study aimed to evaluate the impact of climate change on the habitat suitability of freshwater fish (Z. platypus and N. koreanus) in the five major watersheds of South Korea using EHSM. Two native fish were selected because they are the most representative (dominant or subdominant) species in the five major watersheds of South Korea. Zacco platypus is distributed across South Korea [17] and is most frequently reported as the dominant species [35]. It is an omnivorous species spawning from June to August [36]. Zacco platypus has a broad spectrum of habitat use, from pools to rapids [35]. Nipponocypris koreanus is frequently reported as the subdominant species (mostly next to Z. platypus) in South Korea [35]. It is an omnivore that spawns from May to July [36]. Compared to Z. platypus, N. koreanus prefers upstream regions or the lower stream order [37].

2. Materials and Methods

2.1. Study Sites and Data Collection

Point data were collected from a total of 115 sites in the five major watersheds (Han, Nakdong, Geum, Seomjin, and Yeongsan rivers) of South Korea (Figure 1). The study sites are representative national monitoring stations where present (2008–2015) hydraulic (flow, cross-section area, depth, and velocity), water temperature, and fish survey data are all available. Hydraulic data such as cross-section area, depth, and velocity were acquired from the Han River Flood Control Office (http://www.hrfco.go.kr, accessed on 8 November 2018). Daily flow data were acquired from the Water Resources Management Information System (WAMIS; http://www.wamis.go.kr, accessed on 8 November 2018). Hydraulic surveys were conducted according to a national standard (KS B ISO748) (Korean Standards & Certifications, 2015). In addition, water temperature and fish data were collected from the Water Environment Information System (http://water.nier.go.kr, accessed on 8 November 2018).
Fish surveys were conducted twice a year by the National Aquatic Ecological Monitoring Program under the Korean Ministry of Environment [38]. Fish sampling was conducted in spring and fall using the casting net (10 trials) or skimming net (30 min) methods following national standard protocols [39], ensuring homogeneity and reliability of the data. The identification and counting were conducted and recorded in situ.
Future predictions were made under two representative concentration pathway (RCP) greenhouse gas emission scenarios (namely, RCP 4.5 and 8.5) [40]. Daily air temperature values (average, minimum, and maximum) at present (2008–2015) and future (2030, from 2026 to 2035; 2050, from 2046 to 2055; and 2080, from 2076 to 2085) were collected from the Korea Meteorological Administration (https://data.kma.go.kr, accessed on 18 May 2017). The present and future daily flow data were downloaded from the Model of Integrated Impact and Vulnerability Evaluation of Climate Change (MOTIVE) website (http://motive.kei.re.kr, accessed on 10 October 2019).
The flow data from the MOTIVE were simulated using SWAT (Soil and Water Assessment Tool, version 2012, USDA Agricultural Research Service and Texas A&M AgriLife Research, Austin, TX, USA) [12]. Initially, the five major watersheds were divided into 535 subwatersheds. The subwatershed was further divided into hydrological response units (HRUs) according to environmental characteristics (soil type, land use, and slope). DEM (Digital Elevation Model), soil map, and land use map were acquired from the National Geographic Information Institute (https://www.ngii.go.kr, accessed on 18 January 2016), WAMIS (https://www.wamis.go.kr, accessed on 18 January 2016), and Ministry of Environment (https://egis.me.go.kr/main.do, accessed on 18 January 2016), respectively [41,42]. The flow for each subwatershed was derived by summing the flow of HRUs. The SWAT parameters (e.g., Soil Conservation Service curve number for moisture condition II, the delay time for aquifer recharge, and soil evaporation compensation factor) were calibrated using flow monitoring data (2013–2015) from the Water Environment Information System (http://water.nier.go.kr, accessed on 22 August 2016). The calibration results have shown a high coefficient of determination (R2 = 0.86 and 0.85 in Geum and Seomjin river, respectively) and Nash-Sutcliffe efficiency coefficient (NSE = 0.64 and 0.61 in Geum and Seomjin river, respectively) [41,42], which satisfies the suggested criteria of R2 ≥ 0.6 and NSE ≥ 0.5 [43,44].

2.2. Regression Models for Hydraulic and Thermal Variables

Regression models were applied to calculate hydraulic (water depth and velocity) and thermal (average, maximum, and minimum water temperature) variables at 115 study sites [33]. Daily water depths and velocities were derived from daily flows. First, water level (L) or cross-section area (A) was calculated from water flow (Q) using Equation (1) [45]:
Q = a × Xb + c
where a, b, and c are constants, and X is the water level or cross-sectional area. Depth (D) was calculated using its linear relationship with the water level (Equation (2)) [45] while velocity (V) was calculated by dividing the flow rate with the cross-sectional area (Equation (3)) [45]:
D = a × L + b
V = Q ÷ A
In addition, average water temperature was calculated from the linear relationship between water temperature and air temperature using Equation (4) [46]:
WT = a × AT + b
where WT is the average water temperature; AT is the average air temperature; and a and b are constants. The same regression model was applied to calculate maximum and minimum water temperatures from maximum and minimum air temperatures.

2.3. Response Curves for Hydraulic and Physiologic Habitat Suitability

Hydraulic habitat suitability curves for Z. platypus and N. koreanus were taken from the work of Kang [47] (Figure 2), which defines the central 50, 75, 90, and 95% of fish abundance data (Han and Geum river watersheds in South Korea) as a suitability of 1, 0.5, 0.1, and 0.05, respectively [48,49].
Growth and stress curves of Z. platypus and N. koreanus were developed (Figure 3), according to the method described in a previous study [33]. Fish monitoring data from 2008 to 2016 collected from the Water Environment Information System (http://water.nier.go.kr, accessed on 27 August 2018) were used to derive growth and stress parameters (Table S1). The lower (G1) and upper (G2) optimum temperatures for growth were defined as the points that contained central 50% of the fish monitoring data [48]. Furthermore, the lower (G0) and upper (G3) threshold temperatures for growth were defined as the points that contained central 90% of the data [48].
The threshold for cold stress (C0) of Z. platypus and N. koreanus was set at 6.0 °C by rounding off 6.4 °C, which is the lowest recorded temperature of appearance [47]. The threshold for heat stress (H0) of Z. platypus and N. koreanus was set at 30 °C and 29 °C, respectively, which are the average values of the reported thermal tolerance from Chung et al. [50] and Kang et al. [51]. The rates of cold and heat stress (CR and HR) were estimated using the relationship between the relative fish abundance and water temperature [33], where the lower and upper 25% ranges of fish abundance data (2008–2016) were used. Meanwhile, cold stress was restricted not to elevate below the freezing point of −4.0 °C, which is the minimum water temperature observed in a national monitoring report [52].

2.4. Habitat Suitability Modeling

The annual ecological habitat suitability (EHS) was calculated using EHSM developed in a previous study [33]. As indicated in Figure 4, the annual EHS is the geometric mean of the annual hydraulic habitat stability (HHS) and physiologic habitat suitability (PHS) (Equation (5)).
EHS   = HHS   ×   PHS
The geometric mean assumes that all habitat conditions are equally important [53], and favorable habitat conditions can compensate for unfavorable ones [54].
The annual HHS was calculated as the geometric mean of the suitability indices of water depth and velocity [22]:
HHS = ( ∑ i = 1 365 SI D , i   ×   SI V , i / 365
where SID,i and SIV,i are the suitability indices on day i for water depth (Figure 2a) and velocity (Figure 2b), respectively.
The annual PHS was calculated by multiplying the annual growth (GI) and stress (SI) indices [33]:
PHS = GI × SI
The annual GI and SI were calculated using Equations (8) and (9), respectively:
GI = ( ∑ i = 1 365 G i ) / 365  
where the value of Gi is derived from the growth curves in Figure 3a using the daily average water temperature.
SI = (1 − Min(CSI, 1)) × (1 − Min(HSI, 1))
where CSI and HSI are the annual cold and heat stresses, respectively. It was assumed that stress accumulates monthly in a year and the species’ sensitivity to stress increases over time [34]. Therefore, the stress was weighted as 1 in January and then increased gradually up to 12 in December. The annual CSI and HSI were derived by averaging the weighted monthly cold and heat stresses, respectively (Equation (10)):
CSI   or   HSI = ( ∑ i = 1 12 S i × w i ) / 12
where Si is the average stress in month i, and wi is the weight for month i. Parameter Si was derived from the arithmetic means of the daily cold or heat stress, which was in turn derived from the stress curves in Figure 3b using the daily minimum and maximum water temperatures, respectively.

2.5. Habitat Suitability Model Validation

The EHSM was validated by comparing EHS class with fish abundance. Five EHS classes were derived by applying Jenks natural breaks [55] to EHS values for Z. platypus and N. koreanus (Table S2). Jenks natural breaks define each class by minimizing the variance within a class while maximizing the variance between classes [56]. The abundance of Z. platypus and N. koreanus was determined in terms of catch per unit effort normalized by stream width (CPUEW).

2.6. Statistical Analysis

Matlab’s Curve Fitting Tool (Version R2019a, The Mathworks, Inc., Natick, MA, USA) was used for curve fitting. Statistical Package for the Social Sciences (SPSS) (version 24, IBM Corp, Armonk, NY, USA) was used for the analysis of variance (ANOVA) followed by Tukey’s post hoc test. Partial correlation analysis was conducted using SPSS. All statistical results were determined by a significance level of p < 0.05.

3. Results and Discussion

3.1. Impact of Climate Change on Habitat Suitability

The validation of EHSM has shown good correlations between EHS class and average CPUEW for both Z. platypus and N. koreanus (R2 = 0.874 and 0.951, respectively; Figure S1), indicating the validity of habitat suitability modeling. In general, habitat suitability is closely related to abundance [33,57].
The average EHSs of Z. platypus and N. koreanus at 115 sites under two emission scenarios are listed in Table 1. Under the RCP 4.5 scenario, the EHSs of both species were expected to increase. However, under the RCP 8.5 scenario, the EHSs were expected to decrease after an initial increase in 2030. Especially, changes in PHS seem to follow the trend of EHS change, whereas HHS is expected to decrease under both RCP 4.5 and 8.5 scenarios (Table S3). These findings suggest that PHS-related environmental variables (water temperature) might impact the habitat suitability for Z. platypus and N. koreanus more than HHS-related variables (water depth and velocity). A decrease in average EHS was predicted in 2080 for the RCP 8.5 scenario, where the decrease was predicted to be greater for N. koreanus (19.2%) than for Z. platypus (9.87%).
ANOVA was applied to identify the significant difference (p < 0.05) between present and future EHS (Figure 5, Figures S2 and S3). Under the RCP 4.5 scenario, less than 10% of sites were expected to decrease in EHS significantly in the future. However, under the RCP 8.5 scenario, more sites were predicted to decrease in EHS significantly as a function of time. Therefore, a considerable number of sites were predicted to have degraded habitat suitability for Z. platypus (36.5%) and N. koreanus (44.4%) in 2080 under the RCP 8.5 scenario. However, the habitat degradation was much lower (7.8% and 10.4% sites for Z. platypus and N. koreanus, respectively) under the RCP 4.5 scenario, stressing the need to undertake preventive measures to limit carbon dioxide emission [40]. Freshwater fish are generally vulnerable to climate change under the RCP 8.5 scenario, due to greater changes in the flow and thermal regime [25,26,41,58]. Especially, Ayllón et al. [58] demonstrated that the synergistic effect of altered flow and thermal regime under the RCP 8.5 scenario could lead to the extinction of brown trout in the Eska river, while the population was expected to survive under the RCP 4.5 scenario.
Meanwhile, at most sites (52.2–85.2%), future EHS under the RCP 4.5 and RCP 8.5 scenarios was not significantly different (p ≥ 0.05) from present EHS. Simultaneous increase in growth (GI increase) and stress (SI decrease) with global warming (Table S4) may result in negligible effect of climate change on EHS. In addition, HHS decrease may be compensated by PHS increase (Table S3), likely leading to the insignificant change in EHS under climate change. Similarly, Zhang et al. [27] demonstrated that C. guichenoti will benefit from global warming through extended spawning periods and enhanced habitat conditions for juveniles. Moreover, Morid et al. [25] predicted that increased water temperature will offset beneficial flow conditions decreasing Z. temminickii habitat suitability in summer, while Z. platypus and Z. temminickii were predicted to benefit from increased water temperature in winter regardless of altered flow.

3.2. Identification of Major Environmental Factors

Partial correlation analysis was used to identify the relationship between environmental variables (Table S5) and EHS of Z. platypus and N. koreanus (Table 1). Changes in depth (ΔDepth), average water temperature (ΔWTavg), minimum water temperature (ΔWTmin), and number of hot days (days exceeding H0; ΔHot) were significantly correlated (p < 0.05) to EHS changes in both species (Table 2). Furthermore, the number of cold days (number of days exceeding C0; ΔCold) was also a significantly important factor (p < 0.05) for N. koreanus. Except for ΔDepth, they are environmental variables related to water temperature that affects fish growth and stress.
Water depth was the only hydraulic variable that negatively correlated with EHS change (Table 2). Although the impact of water depth was not as apparent as that of water temperature, it is well known as a hydraulic variable that determines habitat suitability for freshwater fish [21,33,59,60]. The hydraulic habitat suitability for Z. platypus and N. koreanus is prone to decrease when water depth increases, because these fish prefer shallow water (most of the optimal range is under 0.5 m). Similar trends have been reported by Papadaki et al. [22], who reported that habitat suitability for the Salmo farioides (West Balkan trout) decreased owing to a reduction in the depth of water.
Changes in average water temperature were negatively correlated with changes in EHS (Table 2). In general, fish habitat suitability is expected to increase with an increase in water temperature [25,27]. Indeed, fish growth (GI) alone was predicted to increase under climate change (Table S4). However, EHS was decreased owing to a significant increase in fish stress. Notably, most stress-related variables (ΔWTmax, ΔCold, and ΔHot) were negatively correlated with changes in EHS (Table 2).
Based on the partial correlation coefficient (ρ) in Table 2, ΔHot showed the highest correlation with EHS change for both Z. platypus (–0.314) and N. koreanus (–0.326). The partial correlation coefficient can represent the relative sensitivity of each variable [61], suggesting that ΔHot is the most sensitive environmental variable to predict habitat suitability change. Especially, in 2050 and 2080 under the RCP 8.5 scenario, the decrease in average EHS for Z. platypus and N. koreanus (Table 1) corresponded well to the abrupt increase in the number of hot days (Table 3 and Table S5). However, under the RCP 4.5 scenario, the average EHS was increased because of fish growth increase (Table S4) with increasing ΔWTavg (Table 3). Many studies have predicted that freshwater fish will suffer from intensified heat stress due to climate change [7,62,63]. In particular, Butryn et al. [64] demonstrated that the magnitude (temperature above threshold) and duration (time receiving stress) of fish stress are important factors for predicting the thermal habitat suitability for Salvelinus fontinalis (brook trout). Kangur et al. [65] also reported that high water temperature was significantly related to the decrease in Osmerus eperlanus (European smelt) abundance, where the magnitude and duration of hot days were critical factors. These findings suggest that heat stress induced by climate change can be one of the main threats to freshwater fish.

3.3. Implications and Limitations

Fish physiology (e.g., growth, spawning, egg development, survival) is sensitive to water temperature [66,67,68,69]. Generally, the physiological suitability for freshwater fish is optimal within a narrow range of water temperatures and decreases rapidly beyond this range [69,70]. In this respect, the EHSM incorporating fish growth and stress parameters may be able to evaluate the impact of global warming better when compared with the conventional habitat suitability models that do not consider fish physiology [33]. Furthermore, the accumulation of heat and cold stress could strengthen the ability of EHSM to assess the habitat suitability for sensitive fish to climate change [71].
Incorporating physiological constraints (e.g., cold and heat stresses) is a major challenge in habitat suitability modeling [72]. In particular, the relationship between fish growth and/or stress and water temperature should be refined with improved field observation or laboratory experimental data. For instance, survival data against water temperature may provide more accurate heat and cold stress parameters [73]. Selong et al. [74] and Widmer et al. [75] evaluated the survival of bull trout (Salvelinus confluentus) and loach minnow (Rhinichthys cobitis), respectively, under chronic heat stress, which can be used to calculate the heat and cold stress thresholds (H0 and C0) and rates (HR and CR).
Decreased suitable habitat may extirpate freshwater fish population locally [76], which leads to shifts in species distribution [5,62]. This study suggests that N. koreanus is more vulnerable to climate change when compared with Z. platypus (Table 1 and Figure 5). As is evident in Figure 3, N. koreanus prefers lower water temperatures for growth and is more sensitive to heat stress when compared with Z. platypus. Indeed, fish survey data (2008–2016) in South Korea indicated that the median and average water temperatures of Z. platypus habitat (21.6 °C and 21.2 °C, respectively) were higher than those of N. koreanus (19.9 °C and 19.8 °C, respectively) (Table S6). These findings suggest that particular conservation plans are required to preserve the habitat of N. koreanus.

4. Conclusions

EHSM predicted a significant (p < 0.05) habitat suitability decrease for Z. platypus and N. koreanus at 36.5% and 44.4% of 115 sites in South Korea, respectively, in the 2080s under the RCP 8.5 scenario. However, the decrease in habitat suitability was expected to occur at 7.8% and 10.4% of study sites, respectively, under a moderate carbon dioxide emission scenario (RCP 4.5). When compared with hydraulic variables, environmental variables related to water temperature are expected to have a comparatively greater influence on habitat suitability. In particular, the number of hot days appears to be the most critical factor limiting the distribution of Z. platypus and N. koreanus. Although the EHSM can be a promising habitat suitability model for the assessment of climate change impact on freshwater fish, other factors such as anthropogenic disturbance and alien species invasion should be considered further.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w14111825/s1, Figure S1: Relationship between ecological habitat suitability (EHS) class and average catch per unit effort normalized by stream width (CPUEW) for (a) Zacco platypus and (b) Nipponocypris koreanus.; Figure S2: Study sites (total 115) where present habitat suitability (EHS) for Zacco platypus was significantly different (p < 0.05) from future EHS according to ANOVA under RCP 4.5 scenario at (a) 2030 (2026–2035), (b) 2050 (2046–2055), and (c) 2080 (2076–2085) and RCP 8.5 scenario at (d) 2030, (e) 2050, and (f) 2080 (red: decrease, gray: no difference, and green: increase). Figure S3: Study sites (total 115) where present habitat suitability (EHS) for Nipponocypris koreanus was significantly different (p < 0.05) from future EHS according to ANOVA under RCP 4.5 scenario at (a) 2030 (2026–2035), (b) 2050 (2046–2055), and (c) 2080 (2076–2085) and RCP 8.5 scenario at (d) 2030, (e) 2050, and (f) 2080 (red: decrease, gray: no difference, and green: increase); Table S1: Parameters for growth and stress curves of Zacco platypus and Nipponocypris koreanus. Table S3: Average and standard deviation (total 115 sites) of hydraulic (HHS) and physiologic (PHS) suitabilities for Z. platypus and N. koreanus in South Korea at present (2008–2015), 2030 (2026–2035), 2050 (2046–2055), and 2080 (2076–2085). Numbers in the parenthesis indicate percent (%) change compared to present. Table S4: Average growth (GI) and stress (SI) indices for Zacco platypus and Nipponocypris koreanus at 115 sites in South Korea at present (2008−2015), 2030 (2026–2035), 2050 (2046–2055), and 2080 (2076–2085); Table S5: Average values (total 115 sites) of environmental variables (Flowavg = annual average flow, Flowmin = annual minimum flow, Flowmax = annual maximum flow, Depth= annual depth, Velocity= annual velocity, WTavg = annual average water temperature, WTmin = annual minimum water temperature, WTmax = annual maximum water temperature, Cold = number of cold days, Hot = number of hot days) in South Korea at present (2008–2015), 2030 (2026–2035), 2050 (2046–2055), and 2080 (2076–2085); Table S6: Median and average water temperatures for Zacco platypus and Nipponocypris koreanus habitats monitored in South Korea from 2008 to 2016.

Author Contributions

Conceptualization, T.S.; methodology, T.S. and Z.K.; data curation, D.S.; writing—original draft preparation, T.S.; writing—review and editing, J.J.; visualization, T.S. and Z.K.; funding acquisition, T.S. and J.J. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Korea Environment Industry & Technology Institute (KEITI) through the “Climate Change Correspondence Program” (grant number 201400130007) funded by the Korea Ministry of Environment, and by the National Research Foundation of Korea through the “Basic Science Research Program” (grant number NRF-2021R1I1A1A01060115) funded by the Ministry of Education.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Han River Flood Control Office data are available from http://www.hrfco.go.kr, (accessed on 8 November 2018). WAMIS data are available from http://www.wamis.go.kr, (accessed on 8 November 2018). Water Environment Information System data are available from http://water.nier.go.kr, (accessed on 22 August 2016 and 27 August 2018). KMA data are available from https://data.kma.go.kr, (accessed on 18 May 2017). MOTIVE data are available from http://motive.kei.re.kr, (accessed on 10 October 2019).

Acknowledgments

The authors would like to thank the MOTIVE database team for providing the environmental data.

Conflicts of Interest

The authors declare no conflict interest.

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Water 14 01825 g001 550
Figure 1. Study sites (a total of 115) in the five major watersheds (Han, Nakdong, Geum, Seomjin, and Yeongsan rivers) of South Korea for the present study.
Figure 1. Study sites (a total of 115) in the five major watersheds (Han, Nakdong, Geum, Seomjin, and Yeongsan rivers) of South Korea for the present study.
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Water 14 01825 g002 550
Figure 2. Hydraulic suitability curves for Zacco platypus and Nipponocypris koreanus: (a) water depth and (b) velocity (reprinted from the work of Kang [47]). The central 50, 75, 90, and 95% of fish abundance data was defined as a suitability of 1, 0.5, 0.1, and 0.05, respectively.
Figure 2. Hydraulic suitability curves for Zacco platypus and Nipponocypris koreanus: (a) water depth and (b) velocity (reprinted from the work of Kang [47]). The central 50, 75, 90, and 95% of fish abundance data was defined as a suitability of 1, 0.5, 0.1, and 0.05, respectively.
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Water 14 01825 g003 550
Figure 3. Physiologic suitability curves for Zacco platypus and Nipponocypris koreanus: (a) growth and (b) cold and heat stress. The lower (G1) and upper (G2) optimum temperatures for growth were set as 1 and the lower (G0) and upper (G3) threshold temperatures for growth were set as 0. The threshold for cold (C0) and heat (H0) stress were set as 0. The cold stress rate (CR) and heat stress rate (HR) are the slope of cold and heat stress curves, respectively. It was assumed that cold stress is not increased below −4.0 °C.
Figure 3. Physiologic suitability curves for Zacco platypus and Nipponocypris koreanus: (a) growth and (b) cold and heat stress. The lower (G1) and upper (G2) optimum temperatures for growth were set as 1 and the lower (G0) and upper (G3) threshold temperatures for growth were set as 0. The threshold for cold (C0) and heat (H0) stress were set as 0. The cold stress rate (CR) and heat stress rate (HR) are the slope of cold and heat stress curves, respectively. It was assumed that cold stress is not increased below −4.0 °C.
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Figure 4. Schematic diagram of habitat suitability modeling used in this study (modified from the work of Shim et al. [33]).
Figure 4. Schematic diagram of habitat suitability modeling used in this study (modified from the work of Shim et al. [33]).
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Figure 5. Percentage of study sites (total 115) where present ecological habitat suitability (EHS; n = 8) for (a) Zacco platypus and (b) Nipponocypris koreanus is significantly different (p < 0.05) from future EHS (n = 10) according to ANOVA (D: decrease, N: no difference, and I: increase).
Figure 5. Percentage of study sites (total 115) where present ecological habitat suitability (EHS; n = 8) for (a) Zacco platypus and (b) Nipponocypris koreanus is significantly different (p < 0.05) from future EHS (n = 10) according to ANOVA (D: decrease, N: no difference, and I: increase).
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Table
Table 1. Average and standard deviation (total 115 sites) of ecological habitat suitability (EHS) for Z. platypus and N. koreanus in South Korea at present (2008–2015), 2030 (2026–2035), 2050 (2046–2055), and 2080 (2076–2085). Numbers in the parenthesis indicate percent (%) change compared to present.
Table 1. Average and standard deviation (total 115 sites) of ecological habitat suitability (EHS) for Z. platypus and N. koreanus in South Korea at present (2008–2015), 2030 (2026–2035), 2050 (2046–2055), and 2080 (2076–2085). Numbers in the parenthesis indicate percent (%) change compared to present.
ScenarioPeriodZ. platypusN. koreanus
RCP 4.5Present0.313 ± 0.1490.260 ± 0.129
20300.319 ± 0.159 (+1.92%)0.259 ± 0.136 (−0.385%)
20500.333 ± 0.163 (+6.39%)0.275 ± 0.139 (+5.77%)
20800.322 ± 0.161 (+2.88%)0.261 ± 0.136 (+0.385%)
RCP 8.5Present0.314 ± 0.1500.261 ± 0.131
20300.326 ± 0.161 (+3.82%)0.267 ± 0.137 (+2.30%)
20500.308 ± 0.159 (−1.91%)0.245 ± 0.138 (−6.13%)
20800.283 ± 0.163 (−9.87%)0.211 ± 0.144 (−19.2%)
Table
Table 2. Partial correlation coefficient (ρ) between changes in ecological habitat suitability (EHS) and environmental variables (Flowavg = annual average flow, Flowmin = annual minimum flow, Flowmax = annual maximum flow, Depth = annual depth, Velocity = annual velocity, WTavg = annual average water temperature, WTmin = annual minimum water temperature, WTmax = annual maximum water temperature, Cold = number of cold days, Hot = number of hot days).
Table 2. Partial correlation coefficient (ρ) between changes in ecological habitat suitability (EHS) and environmental variables (Flowavg = annual average flow, Flowmin = annual minimum flow, Flowmax = annual maximum flow, Depth = annual depth, Velocity = annual velocity, WTavg = annual average water temperature, WTmin = annual minimum water temperature, WTmax = annual maximum water temperature, Cold = number of cold days, Hot = number of hot days).
TotalΔFlowavgΔFlowminΔFlowmaxΔDepthΔVelocityΔWTavgΔWTminΔWTmaxΔColdΔHot
Z. platypus−0.0090.011−0.045−0.129 *0.043−0.095 *0.110 *−0.054−0.069−0.314 *
N. koreanus0.0380.047−0.033−0.085 *0.035−0.101 *0.117 *−0.065−0.105 *−0.326 *
* p < 0.05
Table
Table 3. Changes in average environmental variables (Flowavg = annual average flow, Flowmin = annual minimum flow, Flowmax = annual maximum flow, Depth = annual depth, Velocity = annual velocity, WTavg = annual average water temperature, WTmin = annual minimum water temperature, WTmax = annual maximum water temperature, Cold = number of cold days, Hot = number of hot days) at 115 sites in South Korea at 2030 (2026–2035), 2050 (2046–2055), and 2080 (2076–2085).
Table 3. Changes in average environmental variables (Flowavg = annual average flow, Flowmin = annual minimum flow, Flowmax = annual maximum flow, Depth = annual depth, Velocity = annual velocity, WTavg = annual average water temperature, WTmin = annual minimum water temperature, WTmax = annual maximum water temperature, Cold = number of cold days, Hot = number of hot days) at 115 sites in South Korea at 2030 (2026–2035), 2050 (2046–2055), and 2080 (2076–2085).
Hydraulic ΔFlowavg (cms)ΔFlowmin (cms)ΔFlowmax (cms)ΔDepth (m)ΔVelocity (m/s)
RCP4.520303.94 ± 13.5−0.022 ± 0.131146 ± 4720.406 ± 0.8230.007 ± 0.069
20506.48 ± 11.5−0.010 ± 0.085251 ± 4780.432 ± 0.8300.023 ± 0.035
20809.86 ± 18.8−0.019 ± 0.111585 ± 13300.434 ± 0.8330.025 ± 0.037
RCP8.520307.25 ± 14.5−0.016 ± 0.106281 ± 5140.059 ± 0.3070.012 ± 0.040
20504.87 ± 9.69−0.022 ± 0.210121 ± 4940.061 ± 0.3090.013 ± 0.036
20807.04 ± 13.5−0.028 ± 0.190101 ± 3160.073 ± 0.3120.022 ± 0.037
Physiologic ΔTavg (°C)ΔTmin (°C)ΔTmax (°C)ΔCold (d)ΔHot (d) *ΔHot (d) **
RCP 4.520300.304 ± 0.3050.163 ± 0.3910.485 ± 0.3180.248 ± 9.02−13.3 ± 14.2−12.7 ± 13.4
20500.785 ± 0.3390.744 ± 0.4070.867 ± 0.345−5.89 ± 9.52−8.47 ± 14.7−7.68 ± 15.2
20801.21 ± 0.3591.21 ± 0.4301.28 ± 0.342−11.4 ± 9.980.650 ± 12.93.21 ± 14.4
RCP 8.520300.378 ± 0.3310.244 ± 0.4680.474 ± 0.567−2.81 ± 11.1−13.5 ± 14.5−11.9 ± 14.4
20501.16 ± 0.3761.04 ± 0.5221.23 ± 0.596−8.61 ± 11.26.07 ± 12.88.94 ± 14.7
20802.37 ± 0.4732.38 ± 0.5752.43 ± 0.462−24.2 ± 13.933.0 ± 17.236.7 ± 17.2
* Z. platypus, ** N. koreanus.
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Shim, T.; Kim, Z.; Seo, D.; Jung, J. National-Scale Assessment of Climate Change Impacts on Two Native Freshwater Fish Using a Habitat Suitability Model. Water 2022, 14, 1825. https://doi.org/10.3390/w14111825

AMA Style

Shim T, Kim Z, Seo D, Jung J. National-Scale Assessment of Climate Change Impacts on Two Native Freshwater Fish Using a Habitat Suitability Model. Water. 2022; 14(11):1825. https://doi.org/10.3390/w14111825

Chicago/Turabian Style

Shim, Taeyong, Zhonghyun Kim, Dongil Seo, and Jinho Jung. 2022. "National-Scale Assessment of Climate Change Impacts on Two Native Freshwater Fish Using a Habitat Suitability Model" Water 14, no. 11: 1825. https://doi.org/10.3390/w14111825

APA Style

Shim, T., Kim, Z., Seo, D., & Jung, J. (2022). National-Scale Assessment of Climate Change Impacts on Two Native Freshwater Fish Using a Habitat Suitability Model. Water, 14(11), 1825. https://doi.org/10.3390/w14111825

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