Beyond taxonomical space: large ‐ scale ecology meets func ‐ tional and phylogenetic diversity

Community ecology traditionally focuses on hypo‐ thetical‐deductive and experimental approaches and often is criticized for narrowing our under‐ standing of nature to local idiosyncrasies, ignoring the importance of historical explanations. On the other hand, approaches taken by macroecologists and biogeographers have been excessively ex‐ ploratory and correlative, with limited success in elucidating the mechanisms responsible for many of the large‐scale patterns we observe in nature (see Gaston & Blackburn 1999, Ricklefs 2008 and references therein). Recognizing that both ap‐ proaches can learn from each other is pivotal in the challenge of integrating data from different scales in order to unravel the ecological and evo‐ lutionary mechanisms that influence current pat‐ terns in biodiversity and ecosystem functioning. Species richness has been the most com‐ mon metric used to represent all aspects of bio‐ logical diversity (from genetic and taxonomic to phenetic diversity). However, species richness alone cannot describe the processes involved in species coexistence and ecosystem functioning and also does not describe properly the differ‐ ences in community structure. In contrast, phy‐ logenetic and functional diversities allow us to understand the relative importance of species composition in terms of evolutionary history and ecological similarities. Phylogenetic diversity (PD) is a biodiversity measure that accounts for the phylogenetic relationship (hence evolutionary his‐ tory) among species, whereas functional diversity (FD) represents how species are distributed in a multidimensional niche space defined by ecologi‐ cal traits. Phylogenetic and functional approaches to community ecology emerged as prominent fields of research in the last decade (Fig. 1), but some‐ how independently and without much crossover in the first years. Early PD measures were pro‐ posed as a tool to select conservation areas, but later the idea was extended to understand how communities are assembled from a regional pool. FD, which initially was considered the holy grail of the biodiversity‐ecosystem functioning agenda, also was rapidly applied as a metric for investigat‐ ing assembly rules (see Pavoine & Bonsall 2011). How could macroecology and biogeography bene‐ fit from these two approaches? The answer lies in understanding what FD and PD should represent and how they relate to each other: while phyloge‐ netic community ecology links evolutionary and biogeographic history to present‐day ecology, functional diversity (as any trait‐based approach) links niche theory to large‐scale approaches, such as macroecology, biogeography or phylogeogra‐ phy. Therefore, combining ecological and phyloge‐ netic frameworks to explain large scale patterns of biodiversity is an important step, taken recently. Large‐scale studies involving PD and FD seems to be increasing at similar rates (Fig.1). Recently, it was shown that both measures can be decom‐ posed into gamma (regional), alpha (local) and beta (turnover) components. Whereas large‐scale studies and any‐scale studies follows a similar trend for beta‐PD, there were few studies with beta‐FD (none at large‐scale). This is perhaps be‐ cause biogeographers and macroecologists were more aware of evolutionary and historical hy‐ potheses, so the conceptual framework of beta‐ PD was likely to be absorbed first. Also, this could reflect the assumption that closely related species should be ecologically more similar than distant related species and, thus, PD should be a good surrogate for FD (in fact this is what most large and local‐scale PD studies used to assume). This traditional assumption is now debated (e.g. Losos 2008), and these two measures may be viewed as complementary, rather than competing, ap‐ proaches (Gómez et al. 2010, Diniz‐Filho et al. 2011, Meynard et al. 2011, Pavoine & Bonsall 2011, Safi et al. 2011). While some large‐scale studies involving PD and FD are exploratory (e.g. Meynard et al. 2011) others have presented hypotheses and predic‐ tions. Safi et al. (2011) investigated global pat‐