Predicting fish species richness and assemblages with climatic, geographic and morphometric factors: A broad-scale study in Chinese lakes

Abstract

a b s t r a c tThe present study was designed to investigate the relative importance of climatic (temperature and precipitation), geographic (altitude) and morphometric (lake area) factors in predicting fish species richness and assemblages in Chinese lakes at a large spatial scale. Two recursive partitioning tree-based approaches: Classification and Regression Trees (CARTs) and Multivariate Regression Trees (MRTs) were employed to generate predictive models respectively. Six fish assemblages were thus defined from the MRT model. The results indicated that lake altitude was the main determinant for predicting fish assemblages in Chinese lakes (30.43%), followed by precipitation of the driest month (10.47%), temperature annual range (3.62%) and annual mean temperature (3.15%). Validated CART model implied that precipitation of driest month, maximum temperature of warmest month and lake area were the main predictors in determining fish species richness patterns. Overall, our results indicated that the altitudinal extent and range of climatic variation was sufficient to overshadow the area effect in predicting fish species richness and assemblages in Chinese lakes. At the macroecological scale, the effect of temperature and precipitation on fish richness and assemblages also suggests future changes in fish diversity as a consequence of climate change.

Large‐scale patterns of fish diversity and assemblage structure in the longest tropical river in Asia

“…So far, many studies on the environmental determinants of fish assemblage structure have reported the main contribution of physicochemical factors (Braaten & Guy, ; Pires et al., ; Trujillo‐Jiménez et al., ), while others revealed a predominant role of climatic factors in structuring the spatial distribution of fish (Buisson et al., ; Guo et al., ; Reash & Pigg, ; Zhao et al., ). However, in our study, the combination of environmental and spatial factors provided a better explanation of the variation in fish assemblages.…”

“…To date, the determination of factors structuring communities remains one of the major objectives in fish ecological studies and it is widely accepted that the structure of communities results from spatial variability of habitat, environmental variability and interactions among the organisms (Albert & Reis, ; Lujan et al., ; Olden et al., ; Zhao, Grenouillet, Pool, Tudesque, & Cucherousset, ). For instance, some authors revealed the prevailing roles of physicochemical factors in structuring fish communities (Pires, Pires, Collares‐Pereira, & Magalhães, ; Tejerina‐Garro, Fortin, & Rodríguez, ), while others reported the dominant effects of climatic factors (Buisson, Blanc, & Grenouillet, ; Guo et al., ). Considering large‐scale patterns, the study of fish communities is always challenging, for example lack of environmental variables at the local scale, rarity of large data sets of fish composition, which are much more informative than simple presence–absence data, and limitation of modelling the nonlinear relationship between biotic and abiotic factors, especially for cross‐border river basins (e.g.…”

Shift towards Opportunistic Life-History of Sleeper in Response to Multi-Decadal Overfishing

“…All three lakes are typical shallow lakes belonging to the Jianghan lake clusters [37]. The three lakes have similar climate conditions and community compositions (e.g., species abundance and trophic structure), and have undergone the same process of fisheries exploitation development [38,39]. These shallow lakes were historically connected to the Yangtze River via floods.…”

Designing a global assessment of climate change on inland fishes and fisheries: knowns and needs

“…Ninety percent of inland fisheries occur in Africa and Asia (Cochrane et al 2009), where temperature increases are expected to exceed the global annual mean warming (Christensen et al 2007). In China, ponds and lakes, where a majority of inland fisheries occur, may be strongly affected by climate change, especially drought and warming (Yu 2009;Yang et al 2016), and models that incorporate precipitation in the driest month, temperature annual range, and annual mean temperature can be used to predict fish assemblages in Chinese lakes (Guo et al 2015). In Viet Nam, river flows upstream of the Mekong River delta in the dry season 2015-2016 were at historic lows due to an El Nino year, and these events are projected to become more frequent and stronger (Kiem et al 2008).…”