Similar fish species composition despite larger environmental heterogeneity during severe hypoxia in a coastal ecosystem

Abstract

Environmental heterogeneity is one of the most influential factors that create compositional variation among local communities. Greater compositional variation is expected when an environmental gradient encompasses the most severe conditions where species sorting is more likely to operate. However, evidence for stronger species sorting at severer environment has typically been obtained for less mobile organisms and tests are scarce for those with higher dispersal ability that allows individuals to sensitively respond to environmental stress. Here, with the dynamics of fish communities in a Japanese bay revealed by environmental DNA metabarcoding analyses as a model case, we tested the hypothesis that larger environmental heterogeneity caused by severe seasonal hypoxia (lower concentration of oxygen in bottom waters in summer) leads to larger variation of species composition among communities. During summer, fish species richness was lower in the bottom layer, suggesting the severity of the hypoxic bottom water. In contrast to the prediction, we found that although the environmental parameters of bottom and surface water was clearly distinct in summer, fish species composition was more similar between the two layers. Our null model analysis suggested that the higher compositional similarity during hypoxia season was not a result of the sampling effect reflecting differences in the alpha or gamma diversity. Furthermore, a shift in the species occurrence from bottom to surface layers was observed during hypoxia season, which was consistent across species, suggesting that the severe condition in the bottom adversely affected fish species irrespective of their identity. These results suggest that larger environmental heterogeneity does not necessarily lead to higher compositional variation once the environmental gradient encompasses extremely severe conditions. This is most likely because individual organisms actively avoided the severity quasi‐neutrally, which induced mass effect‐like dispersal and lead to the mixing of species composition across habitats. By showing counter evidence against the prevailing view, we provide novel insights into how species sorting by environment acts in heterogeneous and severe conditions.

Spatiotemporal dimensions of community assembly

“…Their finding was consistent with the expectation that abiotic filtering is stronger in harsh habitats (Chase, 2007; Guo et al, 2014). However, Shinohara et al (2022) found an opposite pattern in coastal fish communities in a Japanese bay. Compositional differences between bottom and surface water layers were less pronounced during the summer even though the environment is severer in comparison to other seasons (decreased DO).…”

Large scale eDNA database reveals latitudinal diversity gradients with seasonal variations

“…When we explore the global patterns of biodiversity, we need to consider spatiotemporal variation. Seasonal migratory fish appear in areas depending on the time of year and can alter biodiversity distribution patterns with the changing seasons [6][7][8] . In addition to migration, the incidence of vagrant fish that have migrated outside of their native range and are unlikely to persist and reproduce is thought to influence seasonal changes in biodiversity [9][10][11][12] .…”

“…However, a practical approach is the use of environmental DNA (eDNA), which refers to DNA shed into the environment by live, dead, or partially decomposed organisms containing DNA [14,15] . Recent developments in eDNA analysis have established the reliability of eDNA as a noninvasive, highly sensitive monitoring tool for diversity estimation and species composition, especially in aquatic ecosystems [8,[16][17][18] . A remarkable advantage of eDNA is the relatively low survey cost when compared with traditional capture-based survey methods for macro-organisms like fish, which enables us to collect more temporal and spatial frequencies in the field on larger scales [19] .…”

Jaccard dissimilarity in stochastic community models based on the species-independence assumption

“…Among other factors, species differences and spatial heterogeneity both have profound effects on beta-diversity. As well as spatial heterogeneity (Veech & Crist 2007; Báldi 2008; Jankowski et al 2009; Allouche et al 2012; Bar-Massada & Wood 2013; Heino et al 2013; Pomara et al 2013; Astorga et al 2014; Heino et al 2014; Bar-Massada 2015a,b; Royan et al 2015; Bar-Massada & Belmaker 2017; Zorzal-Almeida et al 2017; Ben-Hur & Kadmon 2020; Shinohara et al 2022), species differences can strongly influence beta-diversity. For example, dispersal mode (passive vs. active) and body size (small vs. large) are both strong determinants of beta-diversity patterns as revealed by meta-analyses (Soininen et al 2007; De Bie et al 2012).…”

“…2015; Bar-Massada & Belmaker 2017; Zorzal-Almeida et al . 2017; Ben-Hur & Kadmon 2020; Shinohara et al . 2022), species differences can strongly influence beta-diversity.…”