Contemporary ecological landscape planning is often based on the assumption that small isolated habitat patches sustain relatively few species. Here, we suggest that for shallow lakes and ponds, the opposite can be true for some groups of organisms. Fish communities tend to be poor or even absent in small isolated lakes. However, submerged vegetation is often more abundant in such waterbodies. As a consequence of low fish biomass and high vegetation abundance, the richness of aquatic birds, plants, amphibians and invertebrates is often relatively high in small, shallow, isolated lakes. Although the rarity of fish is in line with expectations from the ruling paradigms about effects of habitat fragmentation in landscape ecology, the relative richness of various other groups of organisms in small ponds is opposite to these expectations. The case of shallow lakes illustrates that incorporating ecological interactions is essential to understanding the potential effects of habitat fragmentation. Single‐species meta‐population approaches may be misleading if ecological interactions are strong. A meta‐community approach that explicitly incorporates biotic interactions, also those involving different trophic levels, is needed. Our diagnosis suggests that connection of isolated habitat fragments may in some cases reduce, rather than enhance, landscape‐level biodiversity, and implies that biodiversity at the regional level will be maximized if the local habitat patches vary widely in size and degree of connectivity.

This study aimed at unraveling the structure underlying the taxon-richness matrix of shallow lakes. We assessed taxon richness of a large variety of food-web components at different trophic levels (bacteria, ciliates, phytoplankton, zooplankton, fish, macro-invertebrates, and water plants) in 98 shallow lakes from three European geographic regions: Denmark (DK), Belgium/The Netherlands (BNL), and southern Spain (SP). Lakes were selected along four mutually independent gradients of total phosphorus (TP), vegetation cover (SUBMCOV), lake area (AREA), and connectedness (CONN). Principal-components analysis (PCA) indicated that taxon diversity at the ecosystem level is a multidimensional phenomenon. Different PCA axes showed associations with richness in different subsets of organism groups, and differences between eigenvalues were low. Redundancy analysis showed a unique significant contribution to total richness variation of SUBMCOV in all three regions, of TP in DK and SP, and of AREA in DK and BNL. In DK, several organism groups tended to show curvilinear responses to TP, but only one was significantly hump shaped. We postulate that the unimodal richness responses to TP that are frequently reported in the literature for many organism groups may be partly mediated by the unimodal response of macrophyte vegetation to lake productivity.

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Biodiversity is structured by multiple mechanisms that are dependent, at least in part, on ecological similarities and differences among species. Integrating traits and phylogenies in diversity metrics may provide deeper insight into community assembly processes across spatial scales. However, different traits are influenced by processes at different spatial scales, and it is not clear how trait-spatial scale mismatches skew our ability to detect assembly patterns. An additional complexity is how phylogenetic distances, which might capture unmeasured traits, reflect spatially dependent processes. Here we analyze a freshwater zooplankton dataset from 91 ponds and show that different traits are associated with processes at different spatial scales. We first assessed the response of individual traits to processes at both α-and β-scales, and then quantified the power of different combinations of traits and phylogenetic distances to reveal environmental and spatial drivers of α-and β-diversity. We found that explanatory power was maximised when we accounted for environmental and spatial drivers with single, but different traits for α-and β-diversity. Using the most appropriate trait for each spatial scale outperformed phylogenetic information, but phylogenetic information outperformed the same traits when these were used at the wrong spatial scale, and all outperformed taxonomic analyses that ignore trait and phylogenetic information. We demonstrate that accounting for species' similarities and differences provides important information about dominant assembly mechanisms at different spatial scales, and that phylogeny is especially useful when measured traits are uninformative at a given spatial scale or when there is lack of trait data. Our study also indicates, however, that trait-scale mismatches among phylogenetically conserved traits may affect the performance of phylogenetic indices compared to indices that account only for the best single trait at each spatial scale.

A better understanding of the ability of organisms to adapt to local selection conditions is essential for a better insight in their ecological dynamics. The study of micro-evolutionary adaptation and its eco-evolutionary consequences is challenging for many reasons and the choice of a suitable model organism is particularly important. In this paper, we explain why monogonont rotifers, through their unique combination of traits, are ideal study organisms for this purpose. With a literature review, we demonstrate the capacity of monogonont populations to adapt to a variety of selection conditions (e.g., salinity, food shortage, elemental limitation, and disturbance regimes) within very short-time frames and highlight some potential eco-evolutionary implications. Although monogononts are increasingly used in ecoevolution-oriented studies, their potential is still underappreciated compared to other model organisms. No doubt the high prevalence of cryptic species complexes and the lack of genomic tools form important obstacles that may discourage researchers to work with this group. Here, we argue that none of these difficulties should prevent monogonont rotifers from becoming commonly used model organisms in micro-evolutionary studies and make suggestions for future research.

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Zooplankton species richness is typically assessed through analysis of active community samples.