Concepts and applications in functional diversity

Abstract

The use of functional analyses in ecology has grown exponentially over the past two decades, broadening our understanding of biological diversity and its change across space and time. Virtually all ecological sub-disciplines recognize the critical value of looking at species and communities from a functional perspective, and this has led to a proliferation of methods for estimating contrasting dimensions of functional diversity. Differences between these methods and their development generated terminological inconsistencies and confusion about the selection of the most appropriate approach for addressing any particular ecological question, hampering the potential for comparative studies and meta-analyses. We show that two general mathematical frameworks for estimating functional diversity are prevailing: those based on dissimilarity matrices (e.g., Rao entropy, functional dendrograms) and those relying on multidimensional spaces, constructed as either binary (convex hulls) or probabilistic hypervolumes. We review these frameworks, discuss their strengths and weaknesses, and provide an overview of the main R packages allowing to perform these calculations. In parallel, we propose a ‘periodic table’ of functional diversity metrics quantifying the richness, divergence, and regularity of species or individuals under each framework. Therefore, this overview offers a roadmap for confidently approaching functional diversity analyses both theoretically and practically.

Combined effects of bird extinctions and introductions in oceanic islands: decreased functional diversity despite increased species richness

“…including extant native and introduced species). For this purpose, we used the kernel.alpha function in the BAT package (Cardoso et al, 2015; Mammola & Cardoso, 2021). Also for each island, we calculated net change in functional richness as the difference between present and original functional richness.…”

“…Similarly, we evaluated the evenness of the total trait space for each island, considering original and present avifaunas, using kernel.evenness in BAT package (Fig. S4; Mammola & Cardoso, 2021), and calculated net change as the difference between the two. We then calculated average values of functional richness and evenness across islands, and respective 95% confidence intervals, for the original and the present avifaunas, and for the net change.…”

“…We estimated originality based on a 0.01 fraction of random points, using the kernel.originality function in the BAT package (Fig. S4; Mammola & Cardoso, 2021). For each island, we calculated the average functional originality of the extinct species in the original avifauna, and of the introduced species in the present avifauna.…”

“…These often follow the Hutchinsonian niche concept (Hutchinson 1957), relying on the position of species or individuals within a multidimensional space. Among these, the convex hull hypervolume is one of the most used, despite some important limitations, such as the assumption that the multidimensional space is homogenously occupied, making it extremely sensitive to outliers (Mammola & Cardoso, 2021; Mammola et al, 2020). To overcome this limitation, new methods have used probabilistic hypervolumes (Blonder et al, 2018), of which the most popular uses high-dimensional kernel density estimations to delineate the shape and volume of the multidimensional space (Carvalho & Cardoso 2020; Mammola & Cardoso, 2021).…”

Combined effects of bird extinctions and introductions in oceanic islands: Decreased functional diversity despite increased species richness

“…To overcome this limitation, new methods have used probabilistic hypervolumes (Blonder et al., 2018), of which the most popular uses high‐dimensional kernel density estimations to delineate the shape and volume of the multidimensional space (Carvalho & Cardoso, 2020; Mammola & Cardoso, 2020). This density‐based approach assumes a heterogeneous trait space, representing variations in point density within the multidimensional space and better reflecting the concept of niche proposed by Hutchinson (Mammola et al., 2021). Point density is higher where more functionally similar species exist and are closer together within the multidimensional space.…”