Different responses of taxonomic diversity and functional diversity to environmental changes: A case study of fish communities in the Zhoushan Fishing Ground, China

doi:10.21203/rs.3.rs-2731904/v1

Download PDF

Research Article

Different responses of taxonomic diversity and functional diversity to environmental changes: A case study of fish communities in the Zhoushan Fishing Ground, China

https://doi.org/10.21203/rs.3.rs-2731904/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 12 Oct, 2023

Read the published version in Aquatic Sciences →

You are reading this latest preprint version

The resilience and responses of fish communities to environmental stressors have been evidenced to associated with the community structure of fish such as fish biodiversity. What remains unknown, however, is that whether different components of biodiversity responds to environmental changes consistently or differently. Traditional researches about biodiversity and its environmental drivers were mostly based on taxonomic level, and often ignored functional traits of fishes. In this study, we investigated the seasonal patterns of fish biodiversity and their environmental drivers. Biodiversity indices were measured by species abundance and functional traits data, respectively and named taxonomic diversity (TD) and functional diversity (FD), respectively. Our results showed significant difference of fish biodiversity indices at least between two seasons with exception for functional evenness (FEve). Compared with TD, FD showed more changeable along season. Both TD and FD were significantly related to several environmental variables at least in one season, and the response of them to environmental changes were different. In addition, environmental variables played important roles in shaped fish community structure in the study area with eight environmental variables (i.e., salinity, temperature, nitrite, DP, IN, TN, silicate, and PO₄^3-) significantly related to taxonomic structure and three variables (i.e., AN, TD, and COD) significantly related to functional structure. These findings provide evidence that trait-based approach is necessary when assess the responses of fish communities to environmental changes.

Fish assemblages

Taxonomic and functional diversity

Seasonal dynamics

Environmental drive mechanism

A trait-based approach

Ubiquitous marine fish biodiversity loss resulted from climate change and over-exploitation has been spread worldwide, and is known to be adverse to ecosystem functioning and service as well as human well-being (Cardinale et al. 2012; Luypaert et al. 2019; Zhang et al. 2020). In order to mitigate biodiversity loss and restore the historical level of high biodiversity for maintain ecosystem service, identifying how fish biodiversity respond to environmental changes should be one urgent priority (Hillebrand et al. 2018). In this context, there were increasing ecologists focused on the relationships between biodiversity and environmental variables, and revealed tight linkages between them (Hale et al. 2011; Nagelkerken et al. 2022). However, traditional researches about the relationships between marine fish biodiversity and environmental variables were mostly focused on taxonomic diversity (TD), while only a few researches revealed how functional diversity (FD) of fish communities respond to environmental changes (Villéger et al. 2017; Lin et al. 2021). In fact, different components of biodiversity respond differently to environmental changes (Halpern et al. 2015). For example, Leclerc et al. (2019) have found that the possibility of functional richness (FRic) to be threatened by habitat modification, urbanization, climate change and other threats was twice of that for species richness of birds across global islands. However, Edie et al. (2017) have found mass extinctions of benthic marine invertebrate shelf species during the Permian-Triassic and Cretaceous-Paleogene extinctions, while only minor declines in their FRic. Since the relationship between species and habitat environmental variables is determined by their functional traits (e.g., morphological, demographic or physiological attributes) (Violle et al. 2007). Studying the responses of biodiversity to habitat environmental changes without considering functional traits can not reveal how evolution of community composition in the context of habitat changes, which can well captured by trait-based approach (Trindade-Santos et al. 2020). However, up to now, gaps still exist in comprehending driving mechanisms of different facets of biodiversity, which hinder our profound insight on dynamics of biodiversity under climate change and over-exploitation. Thus, it is urgent to have a further understanding of the effects of multiple stressors on both TD and FD to more efficiently and effectively protect and conserve marine biodiversity (Avila et al. 2018).

Zhoushan Fishing Ground is one of the most important fishing grounds in China, with high biodiversity (Yu et al. 2010). Over the past several decades, the area suffered serious threats including overfishing and marine pollution due to rapid development of industry and agriculture (Zhou et al. 2018). Consequently, its biodiversity of fishery resource had undergone sharply decrease under multiple stressors (Lu and Zhao 2013). Since the fundamental relationship between biodiversity and ecosystem functioning is well recognized by researchers (Li et al. 2022). Thus, biodiversity conservation is an essential part in sustainable development of ecosystems in the study area, which drive a series of studies about biodiversity and their environmental drivers. To our knowledge, there were several researches have assessed the spatial-temporal patterns of fish populations at taxonomic level in the study area and their relationships with environmental variables (Yu et al. 2010; Zheng et al. 2012). Due to the study area is located at subtropical monsoon zone and four distinctive seasons, its fish biodiversity (i.e., TD) always changeable across seasons because seasonal environmental conditions supporting seasonality of species composition (Yu et al. 2010). Seasonal changes of species composition is mainly result from species loss/gain among seasons (Maynou and Cartes 2000). Whether a species can adapt to environmental changes and successful settlement is determined by their functional traits such as ecological niches and life history strategies (Beukhof et al. 2019). Therefore, understanding the trait-environment relationship is an useful method that provide valuable information when applying functional composition of fish communities in assessment of environmental quality (Pease et al. 2011). However, in the study area, how FD of fish communities distributed over seasons and related to habitat environmental variables is still scant. Especially, It is not clear whether there is a significant relationship between fish functional traits and their habitat environmental variables.

According to historical records, there were 4-quarter fishery resource surveys in the Zhoushan Fishing Ground during 2006 to 2007 conducted by the Zhejiang Ocean University, and habitat environmental variables including temperature, salinity, pH and other physical and chemical environmental variables of the survey stations were recorded. These prerequisites have enabled the research on seasonal patterns of TD and FD of its fish communities, and their environmental drivers. Although fishery community structures may have changed largely during the past few years (FAO 2016), these history data can be used to study the seasonal pattern of biodiversity within a year and its responses to the gradient changes of environment with seasons. Using these history data, we conducted this study and aimed to answer the following questions: (1) how does fish biodiversity including TD and FD changed with season? For the reason of environmental conditions directly determine what species can survival and reproduction in specific habitat, seasonal environmental conditions result in seasonality of species and functional traits composition (Beukhof et al. 2019). Therefore, we hypothesized that both TD and FD significantly different among seasons. (2) How TD and FD related to environmental variables, and which of them was more sensitive to the gradient changes of environmental variables with seasons? Biodiversity is one of the important biological manifestations of environmental conditions (Goulletquer et al. 2014). Environmental changes such as habitat loss and degradation directly could caused biodiversity loss (SCBD 2010). Thus, we hypothesized that both TD and FD significantly related to environmental variables. In addition, the disturbances from habitat environmental changes on organisms often reflected several environmental variables exceeding tolerance threshold of some fishes, and thereby influence their grow, reproduction, and other life history characteristics (Maynou and Cartes 2000). Thus, we hypothesized that FD have more tight correlations with environmental variables compared with TD. (3) Whether does functional traits of fish correlate with environmental variables? It is generally believed that functional traits are products of environment, and the significant trait-environment relationship was demonstrated by other researches (Lin et al. 2021). Thus, we hypothesized that functional traits of fish in the study area significantly related to environmental variables.

Study area

Zhoushan Fishing Ground located in the northwest of the East China Sea, ranging from 29.5°N to 31°N and 120.5 °E to 125 °E (Fig. 1). It has rich fishery resource, supporting feeding and spawning grounds for many important commercial fishes, shrimps, and crabs (Ning et al. 2008). Since the 1970s, immense and intensive fishing efforts combined with serious deterioration of marine environment have altered the community structures of its fishery resource, fish biodiversity suffering a continuous decline and even “No Fish” impasse (Lu and Zhao 2013). To restore high biodiversity and achieve sustainable development of fishery resource in the study area, it is necessary to understand the relationship between fish biodiversity and environmental variables for conduct reasonable and effective fishery management policies.

Sampling and functional traits collection

A total of 18, 19, 19, and 19 stations were investigated in the study area during summer (August 2006), winter (January 2007), spring (April 2007), and autumn (November 2007), respectively (Fig. 1). Fishes were collected using the beam trawl fishing (30 m beam length, and 7 cod-ends) which was equipped at a fishery production vessel named “Zhe Ding Yu No.11132” with 184 kW of host power and 100 t of tonnage. Each station was trawled for about 1 hour at a speed of 2 knots. All catches were identified to species, counted, and weighted.

Here, FD was calculated based on seven functional traits of fish representing mean temperature preference (MTP), maximum depth (MD), growth (K), the ratio between food consumption and biomass (Q/B), trophic level (TL), length at first maturity (LFM), generation time (GT) (Table 1). These functional traits encompassing life history, habitat preference, and physiological which were chosen as they determine species-environment relationships (Villéger et al. 2017; Mclean et al. 2018). The information including these functional traits was taken from Fishbase (Froese et al. 2019).

Table 1 List of functional traits selected from FishBase (Froese and Pauly 2019) and categories used to describe fish.

Functional trait	Abbreviation	Type	Descriptions
Mean temperature preference (°C)	MTP	Environmental trait	The thermal preference of fish indicates their tolerance to temperature changes (Givan et al. 2018; Pauly and Cheung 2017).
Maximum depth (m)	MD	Environmental trait	This information was used as a proxy to describe the environmental conditions in which the species occur (Pinheiro et al. 2019).
Growth (k)	K	Life history trait	The growth coefficient “k” derived from the von Bertalanffy growth function (Froese and Pauly 2019).
(Q/B)	Q/B	Life history trait	The ratio between food consumption (Q) and biomass (B) is used as a measure of trophic interactions, indicating the energy flow in the system (Palomares and Pauly 1998).
Trophic level	TL	Life history trait	This trait represents the position of the species in the food chain and measures “the number of energy-transfer steps to that level” (Froese and Pauly 2019).
Length at first Maturity (mm)	LFM	Reproductive trait	This is the size at which at least 50% of the fish of a certain species enter their reproductive period (FontelesFilho 1989).
Generation time	GT	Reproductive trait	An estimation of the average age at which the species become reproductive (Froese and Pauly 2019).

Environmental variables

In this study, we measured 15 environmental variables, which covering depth (m), temperature (℃), salinity, pH, chemical oxygen demand (mg/L), ammonia nitrogen (mg/L), nitrite (mg/L), nitrate nitrogen (mg/L), inorganic nitrogen (mg/L), dissolved nitrogen (mg/L), total nitrogen (mg/L), phosphate (mg/L), dissolved phosphorus (mg/L), total phosphorus (mg/L), silicate (mg/L). The sampling and measuring methods of marine physical and chemical variables were carried out according to the marine monitoring specifications (GB17378.4-1998). Conductivity-Temperature-Depth (CTD) system (i.e., oceanography instrument to measure the conductivity, temperature, and depth of seawater, XR-420) was used to measured theses variables during in situ survey. All environmental variables were measured in the all stations after trawling over four seasons in the study area.

Data analysis

Dominant species

The index of relative importance (IRI) was applied to represent the dominance of fish as the following formulation (Pinkas et al. 1971):

IRI = (N +M) * F

Where N is the percentage of abundance of a species in total number, M is the percentage of weight of a species in total biomass, F is the frequency of occurrence of a species.

Community-weighted mean of functional traits

To characterize trait-based community structure, we calculated community-weighted mean trait (CWM) for each sampling station in each season (Diaz et al. 2007). In order to avoid disproportional weighted high abundance but small size fish species and low abundance but large size species, we calculate CWM of a single trait as the average trait value in the community by biomass (Beukhof et al. 2019). The CWM of each trait is then calculated as:

Where CWMx is the CWM for trait x, n is the number of species in the community, p_i is the relative biomass of species i in the community, t_iis the trait value of the species i.

Measurement of biodiversity

In this study, we considered species richness, Simpson’s evenness, variance of abundance as three TD indices because they represent size, regularity, and separation of species in the fish community at taxonomic level, respectively (Magurran 2004). Correspondingly, FRic, functional evenness (FEve), and functional divergence (FDiv) were used as three FD indices because they represent above three facets of species in the functional niche space, respectively (Mason et al. 2005). The matrix of functional traits combined with abundance of species were used to calculated FD indices by the function “FD” package in R software (Laliberté and legendre 2010).

Statistical analysis

PRIMER 5.0 statistic package was used to test significant differences of community structure (including taxonomic structure and functional structure) among seasons. First, fish abundance data and CWM_x were log₁₀(x+1) transformed. Second, a Bray-Curtis dissimilarity matrix were calculated. Last, one-way crossed analysis of similarity (ANOSIM) was PPLIED to test the differences among communities based on similarity matrix; a p < 0.05 was considered significance. The relationships among community structure in each sampling station were graphically represented using a non-metric multi-dimensional scaling analysis (NMS).

The Kruskal-Wallis test was used to analysis significant differences of biodiversity indices and functional traits between any two seasons. Pearson correlation analysis were used to test the relationships between biodiversity indices and environmental variables. Statistical significance was accepted at p < 0.05. Before analysis, all fish and environmental data were all log₁₀ (x+1) transformed to meet assumptions of multivariate normality and to moderate the influence of outliers. The SPSS 22.0 statistics package was used to perform these statistical analyses.

In CANOCO 5.0 software package, we applied a redundancy analysis (RDA) to reveal how fish community structures correlated to environmental variables. A canonical correspondence analysis (CCA) was instead of RDA in the analysis of relationships between taxonomic structure and environmental variables because detrended correspondence analyses showed that taxonomic data of fish in the study area had a longer length of gradient (> 4), so that the linear model of CCA was more appropriate than RDA. Before conducted CCA or RDA, independent variables were chosen via forward selection procedures, a p < 0.05 was evaluated by Monte Carlo permutation tests. All data of environmental variables, species abundance, and functional traits were log₁₀(x+1) transformed to minimize the effects of extreme data.

Species and functional traits composition

During the four seasons surveys in 2006 - 2007, a total of 118 fish species were captured in the Zhoushan Fishing Ground. These species differed in their seasonal preference with 61, 58, 65, and 73 species in spring, summer, autumn, and winter, respectively (Supplementary information Table 1). The species composition varied across seasons, either by number or by weight (Fig. 2). The top 10 fish species of IRI in different seasons accounted for 89.8%, 71.4%, 91.7%, and 71.3% by number in spring, summer, autumn, and winter, respectively. Among these species, only Amblychaeturichthys hexanema and Apogon lineatus were captured in all seasons, of which the former was the most important species in spring, summer, and winter, while the later was the most important one in autumn. The second-most-important species was Larimichthys polyactis, Champsodon snyderi，Acropoma japonicum in spring, summer, autumn (and winter), respectively (Table 2).

In terms of functional traits composition of fish communities, five out of seven traits were significant different between at least two seasons (Table 3). Specifically, CWM_Kwas significantly lower in spring than that in summer and winter; CWM_LFMwas significantly lower in autumn than that in spring and winter; CWM_Q/Bwas significantly lower in spring than that in autumn and winter; CWM_MTPwas significantly higher in autumn than that in other three seasons; CWM_GTwas significantly lower in summer than that in other three seasons.

Table 2 The top 10 fish species for Index of Relative Importance (IRI) in different seasons in the Zhoushan fishing ground are indicated in bold. (/ = no data recorded.) .

Species	IRI in Spring	IRI in Summer	IRI in Autumn	IRI in Winter
Amblychaeturichthys hexanema	4843.16	1730.40	5519.48	2546.47
Larimichthys polyactis	1449.38	21.50	2761.21	96.32
Lophiomus setigerus	1077.35	2.60	/	648.83
Chelidonichthys kumu	787.62	/	2397.19	954.86
Sillago japonica	300.91	/	543.68	4.22
Pleuronichthys cornutus	246.10	206.42	1679.72	70.91
Cynoglossus abbreviatus	206.16	118.53	6024.67	190.94
Cynoglossus trigrammus	183.91	/	4075.53	301.82
Apogon lineatus	155.93	279.26	18871.26	571.12
Cynoglossus sinicus	107.27	/	/	1.04
Champsodon snyderi	28.44	733.59	531.34	79.83
Dysomma anguillare	47.72	705.06	2834.53	54.77
Acropoma japonicum	9.25	549.64	6370.72	996.34
Lepidotrigla japonica	/	263.42	/	/
Psenopsis anomala	/	158.16	2302.66	/
Muraenesox cinereus	53.06	143.89	2683.18	58.34
Argyrosomus macrocephalus	71.88	138.18	/	1.46
Minous monodactylus	25.35	80.17	4346.50	0.38
Harpadon nehereus	13.31	24.63	4196.09	583.08
Pseudorhombus arsius	74.55	22.26	4061.06	2.63
Ophichthus apicalis	29.34	12.29	3353.45	/
Pennahia argentata	/	21.83	3328.98	/
Conger myriaster	41.04	/	1758.78	354.52
Collichthys lucidus	2.33	73.83	2327.20	106.73

Table 3 Functional traits composition based on CWM in spring, summer, autumn, and winter in the Zhoushan fishing ground, respectively. Same letters upper right corner indicate significant differences in functional traits between seasons (Kruskal-wallis test, p < 0.05).

Functional traits	Spring	Summer	Autumn	Winter
CWM_K	0.41±0.06^ab	0.54±0.15^a	0.58±0.31	0.55±0.23^b
CWM_LFM	16.99±2.59^a	15.28±3.77	13.54±5.91^ab	17.72±6.64^b
CWM_Q/B	7.54±1.92^ab	8.48±2.12	9.24±2.76^a	9.18±2.79^b
CWM_TL	3.58±0.11	3.62±0.15	3.68±0.17	3.62±0.19
CWM_MD	273.22±249.49	191.51±99.52	167.75±60.69	249.63±138.70
CWM_MTP	19.76±1.86^a	21.14±2.68^bc	23.37±1.90^abd	19.34±2.52^cd
CWM_GT	2.67±0.40^a	2.32±0.43^abc	2.54±0.69^b	2.56±0.59^c

Community structure and biodiversity

The results of One-way ANOSIM testing showed fish communities substantially overlapped but still significantly vary among four seasons (taxonomic structure: Global R = 0.206, p < 0.05; functional structure: Global R = 0.107, p < 0.05). In NMS diagrams, both taxonomic structure and functional structure were scattered in all seasons (Fig. 3).

The results of kruskal-wallis test revealed that inconsistent changes of TD and FD with seasons (Fig. 4). Three TD indices (i.e., species richness, Simpson’s evenness, and variance of abundance) and 2 FD indices (i.e., FRic and FDiv) were significant difference between at least two seasons. Specifically, species richness in summer was significantly lower than that in spring and winter, while FRic in summer was significantly lower than that in other 3 seasons (Fig. 4a,d). Simpson’s evenness was significantly higher in winter than that in autumn, while FEve was non-significantly different between any 2 seasons (Fig. 4b,e). Variance of abundance was significantly higher in summer than that in winter, while FDiv was significantly lower in summer than that in spring and autumn, and significantly higher in autumn than that in winter (Fig. 4c,f).

The relationship between biodiversity to environmental variables

In this study, both TD and FD indices were observed significantly related (Pearson correlation test) to several environmental variables at least in one season (Fig. 5). In spring, species richness was not significantly related to any environmental variables, while FRic was significantly related to COD; Simpson’s evenness was significantly related to three environmental variables including COD, PO₄^3-, and DP, while FEve was significantly related to 10 environmental variables including salinity, COD, AN, NN, IN, DN, TN, PO₄^3-, DP, and silicate; variance of abundance was significantly related to depth and DP, while FDiv was not significantly related to any environmental variables. In summer, species richness was significantly related to depth, IN, DN, and TN, and variance of abundance was significantly related to depth. In autumn, only Simpson’s evenness was found to significantly related to nitrite; In winter, species richness was significantly related to three environmental variables including PO₄^3-, Dp, and TP, while FRic was significantly related to only one environmental variables (i.e., PO₄^3-).

Fish community structure and related to environmental variables

When analyzing the effects of environmental variables on fish community structure, results showed that eight of 15 variables including salinity, DP, IN, TN, silicate, PO₄^3-, nitrite, and temperature were significantly related to taxonomic structure (p < 0.05), and these variables totally explained 51.4% of variance of taxonomic structure, while three variables including AN, TN, and COD were significantly related to functional structure (p < 0.05), and these variables totally explained 97.4% of variance of functional structure (Fig. 6A, D).

Different species and functional traits of fish differently responded to environmental variables. As water temperature or nitrite increased, abundance of Lepidotrigla japonica, Hoplobrotula armata, Chaeturichthys stigmatias, and Aseraggodes kobensis increased while Hippocampus kelloggi, Liparis tanakae, Oxyurichthys macrolepis, and Sebastiscus marmoratus decreased. When salinity increased, Abantennarius nummifer and Bregmaceros mcclellandi became more abundant while Ctenotrypauchen chinensis, Coilia nasus, Arius sinensis, and Eleutheronema tetradactylum became less abundant. The influence of DP, IN, TN, silicate, and PO₄^3- on species abundance were opposite with the influence by salinity (Figure 6A). In terms of the response of functional traits of fish to environmental variables, as AN increased, the value of CWM_LFM increased, while the value of CWM_MTP declined. When TN increased, the value of CWM_MD declined. When COD increased, the value of CWM_K increased, and the values of CWM_MD and CWM_GT declined (Figure 6B).

Seasonal changes of biodiversity and community assemblage

A total of 118 fish species were captured in the study area during 2006-2007 surveys, which is obviously lower than that in the 1990s but higher than that in the current situation (EDPCR 1994; Tang et al. 2021). These results indicate a general trend of biodiversity declines in the study area, which is consistent with many marine ecosystems in the world under multiple stressors from climate change and anthropogenic disturbances (Ceballos et al. 2017). These results also provide evidence of biodiversity conservation, which prompt a series researches about the mechanisms of biodiversity dynamics and their environmental drivers (Pan et al. 2015; Venegas-Li et al. 2019).

As expected, in this study, both TD and FD showed significant difference among seasons with exception for FEve, which consistent with the observation for TD of fish community in the study area by Tang et al. (2021). The seasonality of fish biodiversity indicated that there were species gain and/or loss among seasons. Whether a species survives in a new habitat or not is determined by species-environment relationship (Mclean et al. 2018). In the study area, fish communities showed significant differences among seasons in structure but a certain stability in species composition with 18 species common to four seasons. Idiosyncratic species combined with the changeable abundance of common species contributed to significant differences of taxonomic structure among seasons. For example, Yellow croaker (Larimichthys polyactis) is one of the important commercial species in the study area, which only captured in spring and contributed 14.6% and 5.5% in total number and total weight, respectively. Although Pinkgray Goby (Amblychaeturichthys hexanema) was captured in all seasons, but its stock density showed obviously different among seasons with 68.3%, 21.0%, 1.3%, and 27.8% of total number in spring, summer, autumn, and winter, respectively. Yellow croaker is a typical migratory species with high sensitivity to temperature, and has obvious seasonal stock density (Wang et al. 2022). As one of our results, Yellow croaker and other migratory species such as Lepidotrigla japonica, Chaeturichthys stigmatias and Sebastiscus marmoratus were observed significantly related to temperature (Figure 6A). Therefore, the high gradient changes of temperature with seasons is one of the important variables that caused significant differences of community structure across seasons. Pinkgray Goby, which is a common species with non-migratory behavior, its stock recruitment may most depends on food availability and other oceanographic conditions such as salinity and pH (Han et al. 2013). In fact, salinity also important to community assembly in the study area. As salinity rises and exceeds to extreme thresholds, a few fauna could maintain their abundant but most species died or migrate out their habitat, which result in biodiversity declines (AL-Saas and Salman 2012).

Compared with TD, FD seems to be higher fluctuations among seasons when it comes to FRic and FDiv. This results can be explained by that FD is more easily impacted by species changes compared with other components of biodiversity (Lin et al. 2020). In fact, due to the most fishes have different functional traits in different life history stages (Sun et al. 2020; Malick et al. 2020), our results may underestimated the fluctuations of FD among seasons because of limited data. Thus, considering functional components is an indispensable part in biodiversity researches, which is necessary for comprehensive understanding dynamics of biodiversity and therefore formulated reasonable and effective biodiversity protection policies.

Different responses of TD and FD to environmental changes

Our results support the hypothesis 2th that both TD and FD were significantly related to several environmental variables, especially in spring and winter. These results indicated that not only TD tightly dependent upon environmental variables but also FD is sensitive to environmental changes, which consistent with the observation by Zeng et al. (2022). In addition, the different patterns of the relationships between biodiversity and environmental variables in different seasons highlight the importance of considering seasonal factors when assessing biodiversity dynamics and their responses to environmental changes as suggested by Li et al. (2022).

We found that FD was more sensitive to environmental changes than TD in spring, and opposite in other seasons. The different responses of TD and FD to environmental changes provide evidence of different drive mechanisms of them, which agrees with the findings in other studies (Su et al. 2015; Teichert et al. 2018). According to previous studies, TD and FD often have different responses to environmental changes because of heterogeneous distributions of threats (Halpern et al. 2015), differences of ecological niche among different species (Stamenković et al. 2022), and functional redundancy and/or complementarity (Villéger et al. 2014). Inconsistent responses of TD and FD to environmental changes shed light on the necessity of considering different facets of biodiversity when assessing biodiversity dynamics under anthropogenic disturbances and climate changes. For example, Trindade-Santos et al. (2020) revealed that global fisheries were expanding target species that play various roles in marine fishery ecosystem by studying the changes of FD of catches in the world's large marine ecosystem fishery in the past 65 years. Cilleros et al. (2016) revealed that different processes of fish assembly in European and Amazonian stream via comparing the patterns of TD and FD. Therefore, at least in the study area, we advocate more studies to focus on FD to make up the gap of its knowledge, and ultimately serve for biodiversity conservation.

Environmental drivers for fish assembly

Although only Eight environmental variables (i.e., temperature, salinity, nitrite, PO₄^3-, DP, IN, TN, and silicate) and three variables (i.e., AN, TN, and COD) were significantly related to taxonomic and functional structure of fish communities, respectively. These variables have high interpretation rate for the variance of community structure. These results indicated that fish communities in the study area were mainly determined by environmental conditions, and further reflected in the conspicuous seasonality for habitat environment. Consistent with our results, there were several researches have revealed that the importance of habitat environment in organism assembly (Kraft et al. 2015; Craven et al. 2018). These results support the deterministic model in ecological niche theory which is dominant by environment filtering in community assembly process (Giam and Olden 2015).

We found that environmental variables have more importance in shaping functional structure than that for taxonomic structure, which agrees with the findings in other studies (McLean et al. 2019; Dong et al. 2021). The strong relationships between four functional traits (CWM_MD, CWM_LFM, CWM_GT, and CWM_K) and three environmental variables (AN, TN, and COD) make functional structure sensitive to the gradient changes of these three environmental variables with seasons. Environmental filtering selects fishes that have similar functional traits and within ecological tolerance thresholds (Craven et al. 2018). Demersal fish and small pelagic fish have opposite means of these four functional traits with relative higher MD, LFM, and GT and lower K in the former than that in the latter. Compared to demersal fish, small pelagic fishes are typical r-strategists and more likely to be impacted by marine physical and chemical factors (Brosset et al. 2017). Therefore, significant differences of functional structure among seasons can be explained by strong affection of the gradient changes of MD, LFM, and GT with seasons on small pelagic fishes. According to previous studies, temperature and salinity also strongly correlation with spawning and growth activities of fishes (Lin et al. 2021). Although there were several studies have demonstrated that strong linkage between functional traits of marine organism and environmental variables (Sun et al. 2020; Harvey et al. 2022), but we still unclear how different functional traits of marine fish respond to environmental changes. Especially at present of intensify and frequent changes of marine environment under dual stressors from climate change and over-exploitation, some important marine fishery ecosystem showed trends of miniaturization and lower trophic levels (Heino and Godø 2002; Engelhard et al. 2015). Profound understanding of the impacts of environmental changes on specific functional traits of fish will provide useful information for maintain ecosystem functioning. Therefore, more researches are needed to further explore the relationships between functional traits of fish and environmental variables.

In the present study, both TD and FD showed significant differences at least between two seasons with exception for FEve. Compared with TD, FD showed more sensitive to seasonal changes. Our results revealed tight relationships between environmental variables and fish biodiversity in the Zhoushan Fishing Ground, and the responses of TD and FD to environmental changes were inconsistent among seasons. The key environmental variables that contribute to significant differences of taxonomic structure and functional structure of fish communities among seasons were different. In addition, Environmental variables were found have relative more importance in shaping functional structure than that in shaping taxonomic structure. The strong linkages between four functional traits (CWM_MD, CWM_LFM, CWM_GT, and CWM_K) and three environmental variables (AN, TN, and COD) revealed environmental drive mechanisms for community assembly. Our results provide valuable information for assessing biodiversity dynamics and its conservation under climate changes and over-exploitation.

Funding This research was funded jointly by the Fundamental Research Funds for the Central Universities, China (22016000319151) and the Public Welfare Technology Application Research Project of Zhejiang (LGN21C190009).

Conflicts of interests The authors declare that they have no conflicts of interest of this article.

Availability of data and material The data of fish community composition and environmental variables are available from the authors upon request. The data of fish functional traits used in this study is available at http://www.fishbase.org.

Avila IC, Kaschner K, Dormann CF (2018) Current global risks to marine mammals: Taking stock of the threats. Biological Conservation 221:44–58. https://doi.org/10.1016/j.biocon.2018.02.021
Beukhof E, Dencker TS, Pecuchet L, Lindegren M (2019) Spatio-temporal variation in marine fish traits reveals community-wide responses to environmental change. Marine Ecology Progress Series 610:205–222. https://doi.org/10.3354/meps1 2826
Brosset P, Fromentin JM, Beveren EV, Lloret J, Marques V, Basilone G, Bonanno A, Carpi P, Donato F, Keč VČ, Felice AD, Ferreri R, Gašparević D, Giráldez A, Gücü A, Magdalena I, Leonori I, Palomera I, Somarakis S, Tičina V, ... Saraux C （2017） Spatio-temporal patterns and environmental controls of small pelagic fish body condition from contrasted Mediterranean areas. Progress in Oceanography 151:149–162. https://doi.org/10. 1016/j.pocean.2016.12.002
Cardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, Narwani A, Mace GM, Tilman D, Wardle DA, Kinzig AP, Daily GC, Loreau M, Grace JB, Larigauderie A, Srivastava DS, Naeem S (2012) Biodiversity loss and its impact on human. Nature 486:59–67. https://doi.org/10.1038/nature11148
Ceballos G, Ehrlich PR, Dirzo R (2017) Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. Proceedings of the National Academy of Sciences of the United States of America 114(30):E6089–E6096. https ://doi.org/10.1073/pnas.17049 49114
Cilleros K, Allard L, Grenouillet G, Brosse S (2016) Taxonomic and functional diversity patterns reveal different processes shaping European and Amazonian stream fish assemblages. Journal of Biogeography 43(9):1832–1843. https ://doi.org/10.1111/jbi.12839
Craven D, Hall JS, Berlyn GP, Ashton MS, Van Breugel M (2018) Environmental filtering limits functional diversity during succession in a seasonally wet tropical secondary forest. Journal of Vegetation Science 29(3):511–520. https ://doi.org/10.1111/jvs.12632
Diaz S, Lavorel S, de Bello F, Quetier F, Grigulis K, Robson M (2007) Incorporating plant functional diversity effects in ecosystem service assessments. Proc. Natl. Acad. Sci. U. S. A. 104:20684–20689. https://doi.org/10.1073/pnas.0704716104
Dong J, Zhao L, Sun X, Hu C, Wang Y, Li W (2021) Response of macrobenthic communities to heavy metal pollution in Laoshan Bay, China: A trait-based method. Marine Pollution Bulletin 167:112292. https://doi.org/10.1016/j.marpolbul.2021.112292
Edie SM, Jablonski D, Valentine JW (2017) Contrasting responses of functional diversity to major losses in taxonomic diversity. PNAS 115(4):732–737. https://doi.org/10.1073/pnas.1717636115
EDPCR (Editorial Department of Putuo County Records) (1994) Marine Biography in the Zhoushan Sea Area. (in Chinese).
Engelhard GH, Lynam CP, García-Carreras B, Dolder PJ, Mackinson S (2015) Effort reduction and the large fish indicator: Spatial trends reveal positive impacts of recent European fleet reduction schemes. Environmental Conservation 42:227–236. https://doi.org/10.1017/S0376892915000077
FAO (2016). The State of World Fisheries and Aquaculture. Contributing to food security and nutrition for all. FAO.
Fonteles-Filho AA (1989) Identificação de unidades de estoque. In A. A. Fonteles-Filho (Ed.), Os recursos pesqueiros-biologia e dinâmica populacional (pp. 67–98). Imprensa Oficial do Ceará.
Froese R, Pauly D (2019) Editors. FishBase. World Wide Web electronic publication. www.fishbase.org,version (02/2019).
Giam X, Olden JD (2015) Quantifying variable importance in a multimodel inference framework. Methods in Ecology and Evolution 7:388–397. https://doi.org/10.1111/2041-210X.12492
Givan O, Edelist D, Sonin O, Belmaker J (2018) Thermal affinity as the dominant factor changing Mediterranean fish abundances. Global Change Biology 24:e80–e89. https://doi.org/10.1111/gcb.13835
Hale R, Calosi P, McNeill L, Mieszkowska N, Widdicombe S (2011) Predicted levels of future ocean acidification and temperature rise could alter community structure and biodiversity in marine benthic communities. Oikos 120(5):661– 674. https://doi.org/10.1111/j.1600- 0706.2010.19469.x
Halpern BS, Frazier M, Potapenko J, Casey KS, Koenig K, Longo C, Lowndes JS, Rockwood RC, Selig ER, Selkoe KA, Walbridge S (2015) Spatial and temporal changes in cumulative human impacts on the world’s ocean. Nature Communications 6:7615. https://doi.org/10.1038/ncomms8615
Han D, Xue Y, Ji Y, Ma Q (2013) Feeding ecology of Amblychaeturichthys hexanema in Jiaozhou Bay, China. Chinese Journal of Applied Ecology 24(5):1446–1452. https://doi.org/10.13287/j.1001-9332.2013.0314
Harvey BP, Marshall KE, Harley CDG, Russell BD (2022) Predicting responses to marine heatwaves using functional traits. Trends in Ecology & Evolution 37:20–29. https://doi.org/10.1016/j.tree.2021.09.003
Heino M, Godø OR (2002) Fisheries-induced selection pressures in the context of sustainable fisheries. Bulletin of Marine Science 70:639–656.
Hillebrand H, Thomas B, Gutt J, Hagen W, Metfies K, Meyer B, Lewandowska A (2018) Climate change: Warming impacts on marine biodiversity. Handbook on Marine Environment Protection pp 353–373. https://doi.org/10.1007/978-3-319-60156-4_18
Kraft NJB, Adler PB, Godoy O, James EC, Fuller S, Levine JM (2015) Community assembly, coexistence and the environmental filtering metaphor. Functional Ecology 29(5):592–599. https://doi.org/10.1111/1365-2435.12345
Laliberté E, Legendre P (2010) A distance-based framework for measuring functional diversity from multiple traits. Ecology 91:299–305. https://doi.org/10.1890/08-2244.1
Leclerc C, Villéger S, Marino C, Bellard C (2019) Global changes threaten functional and taxonomic diversity of insular species worldwide. Diversity and Distributions 26:402–414. https://doi.org/10.1111/ddi.13024
Lin L, Deng W, Huang X, Kang B (2020) Fish taxonomic, functional, and phylogenetic diversity and their vulnerabilities in the largest river in southeastern CHina. Ecology and Evolution 11:11533–11548. https://doi.org/10.1002/ece3.7945
Lin L, Deng W, Huang X, Liu Y, Huang L, Kang B (2021) How fish traits and functional diversity respond to environmental changes and species invasion in the largest river in Southeastern China. PeerJ. 9:e11824. https://doi.org/10.7717/peerj.11824
Li Y, Ma S, Fu C, Li J, Tian Y, Sun P, Ju P, Liu S (2022) Seasonal differences in the relationship between biodiversity and ecosystem functioning in an overexploited shelf sea ecosystem. Diversity and Distributions 28(8):1706–1720. https://doi.org/10.1111/ddi.13521
Lu C, Zhao J (2013) An investigation on the countermeasures against “No fish in East China Sea”. Chinese Fisheries Economics 31(06):27–32. (in Chinese with English abstract).
Luypaert T, Hagan JG, McCarthy ML, Poti M (2019) Status of marine biodiversity in the Anthropocene. In S. Jungblut, V. Liebich, & M. Bode-Dalby (Eds.), The Oceans: Our research, our future (pp. 57–82). Springer. https://doi.org/10.1007/978-3-030-20389-4
Magurran A E (2004) Measuring biological diversity. Blackwell Science.
Malick MJ, Hunsicker ME, Haltuch MA, Parker-Stetter SL, Berger AM, Marshall KN (2020) Relationship between temperature and Pacific hake distribution vary latitude and life-history stage. Marine Ecology Progress Series 639:185-197. https://doi.org/10.3354/meps13286
Mason NWH, Mouillot D, Lee WG, Wilson JB (2005) Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos 111:112–118. https://doi.org/10.1111/j.0030- 1299.2005.13886.x
Maynou F, Cartes JE (2000) Community structure of bathyal decapod crustaceans off south-West Balearic Islands (western Mediterranean): seasonality and regional patterns in zonation. Journal of the Marine Biological Association of the United Kingdom 80(05):789–798. https://doi.org/10.1017/S0025315400002769
McLean M, Mouillot D, Auber A (2018) Ecological and life history traits explain a climate‐induced shift in a temperate marine fish community. Mar. Ecol. Prog. Ser. 606:175–186. https://doi.org/10.3354/meps1 2766
McLean M, Mouillot D, Lindegren M, Villéger S, Engelhard G, Murgier J, Auber A (2019) Fish communities diverge in species but converge in traits over three decades of warming. Global Change Biology 25:3972–3984. https://doi.org/10.1111/gcb.14785
Nagelkerken I, Connell SD (2022) Ocean acidification drives global reshuffling of ecological communities. Global Change Biology 28(23):7038–7048. https://doi.org/10.1111/gcb.16410
Ning P, Yu C, Yu C, Chen Q (2008) The faunal characteristics of fish in the offshore waters of southern Zhejiang Province. Journal of Zhejiang Ocean University (Natural Science) 27:266–270. (in Chinese with English abstract).
Palomares MLD, Pauly D (1998) Predicting food consumption of fish populations as functions of mortality, food type, morphometrics, temperature and salinity. Marine and Freshwater Research 49(5):447–453. https://doi.org/10.1071/MF98015
Pan J, Marcoval MA, Bazzini SM, Vallina MV, Marco SGD (2015) Marine ecology in a changing world. Chapter 2: Coastal marine biodiversity Challenges and threats. https://doi.org/10.1201/b16334-3.
Pauly D, Cheung WWL (2017) Sound physiological knowledge and principles in modeling shrinking of fishes under climate change. Global Change Biology 24:e15–e26. https://doi.org/10.1111/gcb.13831
Pease A.A (2011) Patterns in functional structure and diversity of stream fish assemblages related to environmental factors at multiple scales. Doctoral dissertation, Texas A&M University. Available electronically from https://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8588
Pinkas L, Oliphant MS, Iverson ILK (1971) Food habits of albacore, blueﬁn tuna and bonito in Californian waters. Fish Bulletin 153:11–105.
Pinheiro HT, Shepherd B, Castillo C, Abesamis R, Copus JM, Pyle R, Greene BD, Coleman RR, Whitton RK, Thillainath EC, Bucol AA, Birt M, Catania D, Bell MV, Rocha L (2019) Deep reef fishes in the world’s epicenter of marine biodiversity. Coral Reefs 38:985–995. https://doi.org/10.1007/s00338- 019-01825-5
SCBD (Secretariat of the Convention on Biological Diversity) (2010) Global biodiversity outlook 3. Montréal, Canada: Secretariat of the Convention on Biological Diversity.
Stamenković O, Piperac MS, Čerba D, Milošević D, Ostojić A, Ðorđević NB, Simić SB, Cvijanović D, Buzhdygan OY (2022) Taxonomic and functional aspects of biodiversity and composition of plankton communities in shallow lentic ecosystems along the human impact and environmental gradients. Aquatic Sciences 84(57):1–19. https://doi.org/10.1007/s00027-022-00893-0
Su G, Xu J, Akasaka M, Molinos JG, Matsuzaki SS (2015) Human impacts on functional and taxonomic homogenization of plateau fish assemblages in Yunnan, China. Global Ecology and Conservation, 4:470–478. https://doi.org/10.1016/j.gecco.2015.09.002
Sun P, Chen Q, Fu C, Zhu W, Li J, Zhang C, Yu H, Sun R, Xu Y, Tian Y (2020) Daily growth of young-of-the-year largehead hairtail (Trichiurus japonicus) in relation to environmental variables in the East China Sea. Journal of Marine Systems 201:103243. https://doi.org/10.1016/j.jmarsys.2019.103243
Tang J, Yu N, Zheng J, Xu Y, Yu C (2021) Fish community structure and diversity in the Zhoushan Fishing Ground near Daxiao Yushan. Journal of Zhejiang Ocean University (Natural Science). 40(3):209–217. (in Chinese with English abstract).
Teichert N, Lepage M, Chevillot X, Lobry J (2018) Environmental drivers of taxonomic, functional and phylogenetic diversity (alpha, beta and gamma components) in estuarine fish communities. Journal of Biogeography, 45:406– 417. https://doi.org/10.1111/jbi.13133
Trindade-Santos I, Moyes F, Magurran E (2020) Global change in the functional diversity of marine fisheries exploitation over the past 65 years. Proceedings of the Royal Society B Biological Sciences 287:20200889. https://doi.org/10.1098/rspb.2020.0889
Venegas-Li R, Levin N, Morales-Barquero L, Kaschner K, Garilao C, Kark S (2019) Global assessment of marine biodiversity potentially threatened by offshore hydrocarbon activities. Global Change Biology 25(6):2009–2020. https://doi.org/10.1111/gcb.14616
Villéger S, Brosse S, Mouchet M, Mouillot D, Vanni MJ （2017） Functional ecology of fish: Current approaches and future challenges. Aquatic Sciences 79：783−801. https://doi.org/10.1007/s00027-017-0546-z
Villéger S, Grenouillet G, Brosse S （2014） Functional homogenization exceeds taxonomic homogenization among European fish assemblages. Global Ecology and Biogeography 23：1450–1460. https://doi.org/10.1111/geb.12226
Violle C, Navas ML, Vile D, Kazakou E, Fortunel C, Hummel I, Garnier E (2007) Let the concept of trait be functional! Oikos 116:882−892. https://doi.org/10.1111/j.0030-1299.2007.15559.x
Wang Y, Wang J, Zhou Y, Xu K, Jiang R, Li Z, Zhu H, Wang J, Cui G (2022) Spatial and temporal distribution characteristics of Larimichthys polyactis in Zhoushan fishing ground and the adjacent waters based on two-stage GAM. Journal of Fishery Sciences of China 29(4):633–641. (in Chinese with English abstract).
Yu C, Chen Q, Chen X, Ning P, Zheng J (2010) Species composition and quantitative distribution of fish in the Zhoushan Fishing Ground and its adjacent waters. Oceanologia et Limnologia Sinica 41(3):410–417. (in Chinese with English abstract).
Zeng Z, Cheung WWL, Lai H, Yi H, Bi S, Li H, Chen X, Su Y, Liu X, Chen Q, Zhang Z, Wei X, Chen J, Li G (2022) Species and functional dynamics of the demersal fish community and responses to disurbances in the Pearl River Estuary. Frontiers in Marine Science 9:921595. https://doi.org/10.3389/fmars.2022.921595
Zhang K, Guo J, Xu Y, Jiang Y, Fan J, Xu S, Chen Z (2020) Long-term variations in fish community structure under multiple stressors in a semi-closed marine ecosystem in the South China Sea. Science of the Total Environment 745:140892. https://doi.org/10.1016/j.scitotenv.2020.140892
Zheng X, Yu C, Chen X, Zheng J, Ning P, Jiang X (2012) Species composition and spatio-temporal distribution of crabs in Zhoushan Fishing Ground and its adjacent areas. Oceanologia et Limnologia Sinica 43(1):147–153. (in Chinese with English abstract).
Zhou S, Pan Y, Zhang L, Xue B, Zhang A, Jin M (2018) Biomagnification and enantiomeric profiles of organochlorine pesticides in food web components from Zhoushan fishing ground, China. Marine Pollution Bulletin 131:602–610, http://doi.org/10.1016/j.marpolbul.2018.04.055

No competing interests reported.

Supplementaryinformation.doc

Download PDF

Journal Publication

published 12 Oct, 2023

Read the published version in Aquatic Sciences →

Editorial decision: Major revision
05 Jul, 2023
Reviews received at journal
04 Jul, 2023
Reviewers agreed at journal
19 Jun, 2023
Reviews received at journal
28 Apr, 2023
Reviewers agreed at journal
14 Apr, 2023
Reviewers invited by journal
05 Apr, 2023
Editor assigned by journal
28 Mar, 2023
Submission checks completed at journal
27 Mar, 2023
First submitted to journal
24 Mar, 2023

You are reading this latest preprint version

Different responses of taxonomic diversity and functional diversity to environmental changes: A case study of fish communities in the Zhoushan Fishing Ground, China

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Materials And Methods

Results

Discussion

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1