Elsevier

Ecological Indicators

Volume 25, February 2013, Pages 279-287
Ecological Indicators

Assessing biodiversity with sound: Do acoustic diversity indices reflect phylogenetic and functional diversities of bird communities?

https://doi.org/10.1016/j.ecolind.2012.10.009Get rights and content

Abstract

Measuring biodiversity is a challenging task for research in taxonomy, ecology and conservation. Biodiversity is commonly measured using metrics related to species richness, phylogenetic-, or functional-trait diversity of species assemblages. Because these metrics are not always correlated with each other, they have to be considered separately. A descriptor of animal diversity based on the diversity of sounds produced by animal communities, named here the Community Acoustic Diversity (CAD), was recently proposed. In many cases, the CAD could be easier to measure than other metrics. Although previous analyses have revealed that acoustic diversity might increase as species richness increases, the ability of CAD to reflect other components of biodiversity has not been formally investigated. The aim of this study is to test theoretically whether functional and phylogenetic diversities could be reflected by acoustic diversity indices in bird communities. Data on species assemblages were collated by the French Breeding Bird Survey describing spatial and temporal variation in community structure and composition across France since 2001. Phylogenetic and functional data were compiled from literature. Acoustic data were obtained from sound libraries. For each of the 19,420 sites sampled, indices of phylogenetic, functional and acoustic diversity of bird communities were calculated based on species’ pair-wise distance matrices and species’ abundances. The different aspects of biodiversity were compared through correlation analyses. The results showed that acoustic diversity was correlated with phylogenetic diversity, when the branch lengths of the tree were considered, and to functional diversity, especially body mass and reproduction. Correlations between phylogenetic, functional and acoustic distances among species did not entirely explain the correlations between phylogenetic, functional and acoustic diversity within communities. This result was interpreted as an effect of local ecological processes underpinning how bird communities assemble. Comparing the diversity patterns with a null model, phylogenetic and functional diversities were significantly clustered whereas acoustic diversity was not different from what was expected by chance. A comparison between acoustic indices showed that spectral component of acoustic diversity seems more appropriate to reveal bird phylogenetic diversity whereas temporal component seems more adapted to reveal functional diversity of a bird community. Overall, even if the processes at the origin of the different facets of biodiversity are different, CAD reveals part of phylogenetic diversity and some extent of functional diversity.

Highlights

► Acoustics is a developing tool for animal diversity assessment. ► Acoustic diversity of 19,420 French bird communities was theoretically analyzed. ► Correlations between acoustic, phylogenetic, functional diversities were computed. ► Acoustic and phylogenetic diversities were correlated when considering branch lengths. ► Acoustic diversity was correlated with body mass and reproduction diversity.

Introduction

Diversity indices have been growing in number, but have been developed separately by different research fields related to evolution or ecology (Pavoine and Bonsall, 2011). At the community level, indices mostly used to describe biodiversity include species richness, species relative abundance, and rarity. However, several recent studies have shown that these traditional metrics are insufficient to capture all facets of diversity and have proposed to focus instead on species functional and phylogenetic distinctiveness. Functional diversity is measured from functional traits that describe a variety of roles that different organisms play in their ecosystem (Petchey and Gaston, 2002). Depending on the ecosystem process analyzed, these traits might depend, for instance, on physiological, life-history, morphological, ecological or behavioral characteristics. In the last two decades, there has been an increasing number of functional diversity studies focusing on the local biotic and abiotic interactions that could explain the processes of species assemblages (Pavoine and Bonsall, 2011, Pillar et al., 2009). Phylogenetic diversity measures the dispersion of species belonging to a community over a phylogenetic tree (Faith, 1992, Pavoine et al., 2005a, Vane-Wright et al., 1991). In theory, phylogenetic diversity can reflect functional diversity if functional traits have a strong phylogenetic signal and thus if the phylogenetic distance between any two species reflects their difference in terms of a combination of functional traits (Webb et al., 2002). In practice, few studies have compared results collected from different aspects of biodiversity and some have shown that these were only partially correlated (Devictor et al., 2010, Pavoine et al., 2005a, Petchey and Gaston, 2002).

Working on phylogenetic and functional diversities can shed light on ecological processes. Indeed, functional traits could be affected by local ecological processes including competition and environmental filters. Competition leads to functional diversification contrary to environmental filters. Therefore, low functional diversity might be obtained with high species richness and abundance if environmental constraints filter species with similar traits (Holdaway and Sparrow, 2006, Petchey et al., 2007). Moreover, an evolutionary convergence of the functional traits of distantly related lineages can lead communities to include many lineages (high phylogenetic diversity) but which are functionally similar (low functional diversity) (e.g. Cavender-Bares et al., 2004, Grandcolas, 1993, Grandcolas, 1998). In addition, in regions where species recently emerged by rapid radiation, high species richness might be attained with low phylogenetic diversity as species have diverged only recently (Slingsby and Verboom, 2006). Overall, although phylogeny and functional trait variations are becoming frequently used to assess biodiversity, they do not necessarily correlate due to evolutionary and ecological processes and have to be considered separately.

Beyond these components of biodiversity, acoustic diversity recently emerged as a possible relevant indicator of biodiversity for several reasons (Obrist et al., 2010, Sueur et al., 2008b). First, there are practical advantages to using passive acoustic methods in conservation surveys. Acoustics allows exploring habitats that are difficult to access such as canopy (Riede, 1997), marine and freshwater habitats (Luczkovich et al., 2008), soil and interior structures of plant (Mankin et al., 2000), and dark environments (Meyer et al., 2011, Obrist et al., 2004). Moreover, compared to classic methods of biodiversity assessment based on field inventories, passive acoustic censuses are less costly especially when automatic recordings are used which can ensure large temporal and spatial scale surveys. The description of sound provides information on acoustic diversity at individual (Pollard et al., 2010), population (Dawson and Efford, 2009, Forrest, 1988), species (Brandes et al., 2006, Skowronski and Harris, 2006), community (Cardoso and Price, 2010, Diwakar and Balakrishnan, 2007, Riede, 1997, Riede, 1993) and landscape levels (Pijanowski et al., 2011). At the landscape scale, a soundscape ecological approach distinguishes three components in the soundscape, namely the biophony, the geophony and the anthrophony. Biophony is mainly defined as “a collection of sounds produced by all living organisms in a given habitat over a specified time” (Pijanowski et al., 2011). The present paper considers the diversity of the acoustic community here named for the first time Community Acoustic Diversity (CAD). Biophony is very closely related to acoustic community. However, the concept of community includes not only the species assemblage but also the interactions between species that are supposed to compete for the acoustic resource. The CAD considers the competitive interaction for the sound space, structured by the sound resource. Contrary to biophony, community acoustic diversity intends to reflect all ecological processes that determine how many diverse biotic sounds co-occur in a community. Acoustic output of an animal community is generally analyzed using three approaches: species identification by an expert, species identification using automatic recognition and global acoustic measure without species identification. As species identification of singing individuals is necessary and requires a high level of expertise, identification has to be achieved by trained experts (Dickinson et al., 2010) or by automatic classification processes (e.g. Acevedo et al., 2009, Han et al., 2011). Both approaches are difficult to apply due to song overlap observed in rich acoustic communities. Consequently, new indices have been recently developed to obtain a global measure of acoustic diversity without any species identification. For instance, an entropy-like index based only on the frequencies recorded was proved to increase with the number of species. Likewise, a dissimilarity index based on the variations of frequencies collected was shown to be inversely correlated with the number of shared species between two focal communities (Depraetere et al., 2012, Sueur et al., 2008b). An acoustic variability index was also shown to evolve with the dynamics of bird communities (Farina et al., 2011, Pieretti et al., 2011).

These approaches generally support a correlation between the CAD and species richness, and the CAD and abundance, but whether and how CAD can be related to other biodiversity components has never been investigated. Rather than working on pure simulated communities as Sueur et al., 2008a, Sueur et al., 2008b did it first, we chose to test the dissimilarity index and to try to understand the information it can provide by working on bird communities that were described through data gathered by local field workers. A theoretical analysis was performed on data extracted from different sources: (i) bird community composition from the French breeding bird survey, (ii) phylogenetic and functional data corresponding to each species from literature and (iii) acoustic data corresponding to each species from different sound libraries to answer the following questions: (i) Do spectral and temporal parts of an acoustic signal have different impacts on acoustic diversity calculation? (ii) Does the acoustic diversity reflect phylogenetic and/or functional diversities of bird communities? (iii) Do the acoustic, phylogenetic and functional diversities result from the same ecological and evolutionary processes?

Section snippets

Materials and methods

Acoustic, phylogenetic and functional diversity indices were computed using the quadratic entropy index (Chave et al., 2007, Pavoine et al., 2004, Pavoine et al., 2005b, Pavoine and Bonsall, 2011, Rao, 1986). The quadratic entropy index was applied to abundance data describing bird communities and a pair-wise distance matrix among species for each diversity type (i.e. acoustic, phylogenetic, and functional diversity) (Fig. 1). Two types of data were necessary to calculate diversity indices: the

Species level

Correlations between the acoustic distances were all significant (Table 1). All distances associated with the spectral component only (i.e. Df, KL and KS) were highly correlated. Even if high, the lowest correlations were between 1  RV and the other indices. Due to high redundancy between Df, KS and KL and because these three indices measure the same sound component, only Df and 1  RV indices were considered in the following analyses.

Correlations between the acoustic distances and the functional

Discussion

Interest in using acoustic methods as a new tool for the identification of singing species, the monitoring of species or populations, or the evaluation of global acoustic diversity is rapidly increasing (Acevedo et al., 2009, Blumstein et al., 2011, Obrist et al., 2010). In particular, the evaluation of global acoustic diversity (e.g. Sueur et al., 2008b) appears as an innovative method in conservation biology as it can be deployed over large spatial and temporal scales and can facilitate the

Acknowledgements

This work was supported by a CNRS INEE PhD grant and the FRB BIOSOUND program. Authors would like to thank Pierre-Yves Henry, Marsha Schlee, Grégoire Lois, Philippe Clergeau, Camila Andrade for their help when we collecting bird functional traits. We greatly thank the hundreds of volunteers who have been taking part in the national breeding bird survey (FBBS program). We thank Ryan Calsbeek for improving the language of the manuscript and the two anonymous referees for their helpful comments on

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