Spatio‐temporal scaling of biodiversity and the species–time relationship in a stream fish assemblage

1. The increase of species richness with the area of the habitat sampled, that is the species–area relationship, and its temporal analogue, the species–time relationship (STR), are among the few general laws in ecology with strong conservation implications. However, these two scale‐dependent phenomena have rarely been considered together in biodiversity assessment, especially in freshwater systems. 2. We examined how the spatial scale of sampling influences STRs for a Central‐European stream fish assemblage (second‐order Bernecei stream, Hungary) using field survey data in two simulation‐based experiments. 3. In experiment one, we examined how increasing the number of channel units, such as riffles and pools (13 altogether), and the number of field surveys involved in the analyses (12 sampling occasions during 3 years), influence species richness. Complete nested curves were constructed to quantify how many species one observes in the community on average for a given number of sampling occasions at a given spatial scale. 4. In experiment two, we examined STRs for the Bernecei fish assemblage from a landscape perspective. Here, we evaluated a 10‐year reach level data set (2000–09) for the Bernecei stream and its recipient watercourse (third‐order Kemence stream) to complement results on experiment one and to explore the mechanisms behind the observed patterns in more detail. 5. Experiment one indicated the strong influence of the spatial scale of sampling on the accumulation of species richness, although time clearly had an additional effect. The simulation methodology advocated here helped to estimate the number of species in a diverse combination of spatial and temporal scale and, therefore, to determine how different scale combinations influence sampling sufficiency. 6. Experiment two revealed differences in STRs between the upstream (Bernecei) and downstream (Kemence) sites, with steeper curves for the downstream site. Equations of STR curves were within the range observed in other studies, predominantly from terrestrial systems. Assemblage composition data suggested that extinction–colonisation dynamics of rare, non‐resident (i.e. satellite) species influenced patterns in STRs. 7. Our results highlight that the determination of species richness can benefit from the joint consideration of spatial and temporal scales in biodiversity inventory surveys. Additionally, we reveal how our randomisation‐based methodology may help to quantify the scale dependency of diversity components (α, β, γ) in both space and time, which have critical importance in the applied context.     

Can stochastic geographical evolution re‐create macroecological richness–environment correlations?

Aim Richness gradients are frequently correlated with environmental characteristics at broad geographic scales. In particular, richness is often associated with energy and climate, while environmental heterogeneity is rarely its best correlate. These correlations have been interpreted as evidence in favour of environmental determinants of diversity gradients, particularly energy and climate. This interpretation assumes that the expected‐by‐random correlation between richness and environment is zero, and that this is equally true for all environmental characteristics. However, these expectations might be unrealistic. We investigated to what degree basic evolutionary/biogeographical processes occurring independently of environment could lead to richness gradients that correlate with environmental characteristics by chance alone. Location Africa, Australia, Eurasia and the New World. Methods We produced artificial richness gradients based on a stochastic simulation model of geographic diversification of clades. In these simulations, species speciate, go extinct and expand or shift their distributions independently of any environmental characteristic. One thousand two hundred repetitions of this model were run, and the resulting stochastic richness gradients were regressed against real‐world environmental variables. Stochastic species–environment relationships were then compared among continents and among three environmental characteristics: energy, environmental heterogeneity and climate seasonality. Results Simulations suggested that a significant degree of correlation between richness gradients and environment is expected even when clades diversify and species distribute stochastically. These correlations vary considerably in strength; but in the best cases, environment can spuriously account for almost 80% of variation in stochastic richness. Additionally, expected‐by‐chance relationships were different among continents and environmental characteristics, producing stronger spurious relationships with energy and climate than with heterogeneity. Main conclusions We conclude that some features of empirical species–environment relationships can be reproduced just by chance when taking into account evolutionary/biogeographical processes underlying the construction of species richness gradients. Future tests of environmental effects on richness should consider structure in richness–environment correlations that can be produced by simple evolutionary null models. Research should move away from the naive non‐biological null hypotheses that are implicit in traditional statistical tests.