Linking acoustic diversity to compositional and configurational heterogeneity in mosaic landscapes

Abstract

Context There is a long-standing quest in landscape ecology for holistic biodiversity metrics accounting for multi-taxa diversity in heterogeneous habitat mosaics. Passive Acoustic Monitoring of biodiversity may provide integrative indices allowing to investigate how soundscapes are shaped by compositional and configurational heterogeneity of mosaic landscapes.Objectives We tested the effects of dominant habitat and landscape heterogeneity on acoustic diversity indices across a large range of mosaic landscapes from two long-term socio-ecological research areas in Occitanie, France and Arizona, USA.Methods We assessed acoustic diversity by automated recording for 44 landscapes distributed along gradients of compositional and configurational heterogeneity. We analyzed the responses of six acoustic indices and a composite multiacoustic index to habitat type and multi-scale landscape metrics for three time periods: 24hr-diel cycles, dawns and nights. Results Landscape mosaics dominated by permanent grasslands in Occitanie and woodlands inArizona produced the highest values of acoustic diversity. Moreover, several indices including H, ADI, NDSI, NP and the multiacoustic index consistently responded to edge density in both study regions, but with contrasting patterns, increasing in Occitanie and decreasing in Arizona. Landscape configuration was a key driver of acoustic diversity for diel and nocturnal soundscapes, while dawn soundscapes depended more on landscape composition.Conclusions Acoustic diversity correlated more with configurational than compositional heterogeneity in both regions, with contrasting effects explained by the interplay between biogeography and land use history. We suggest that multiple acoustic indices are needed to properly account for complex responses of soundscapes to large-scale habitat heterogeneity in mosaic landscapes.

Decomposing drivers in avian insectivory: large-scale effects of climate, habitat and bird diversity

“…They therefore correspond to soundscapes containing multiple vocalizing species (Sueur et al, 2008; Villanueva-Rivera, Pijanowski, Doucette & Pekin, 2011). Acoustic entropy is also known to respond significantly to local forest habitat (Barbaro et al, 2022), which is generally a good predictor of bird occupancy probability (Morante-Filho, Benchimol & Faria, 2021).…”

“…Thus, also local factors can alter macroecological patterns by filtering the regional species pool (De la Mora, García-Ballinas & Philpott, 2015;Kleijn, Rundlöf, Scheper, Smith & Tscharntke, 2011). Forest cover proved to be a good predictor of biodiversity, including acoustic diversity, likely due to the complex architecture of trees and diversity of resources and trophic interactions between trees, insect herbivores and their natural enemies (Barbaro et al, 2022;Long & Frank, 2020). Birds are particularly sensitive to forest cover at different scales, which influences both the amount of available prey and nesting sites (Lee, Fahrig, Freemark & Currie, 2002;Mazerolle & Villard, 1999;Rega-Brodsky & Nilon, 2017).…”

Using acoustic indices in ecology: Guidance on study design, analyses and interpretation

“…Some contradictory findings may arise from issues in research design (see considerations below), but other inconsistencies reflect soundscape differences among species, acoustic communities, biomes and ecosystems (Barbaro et al, 2022;Buxton et al, 2018;Eldridge et al, 2018).…”

“…Phillips et al, 2018; Ross et al, 2018; Scarpelli et al, 2021). This applies across ecological domains—dawn or dusk choruses are readily detectable in habitats as diverse as temperate grassland, tropical rainforest and coral reefs (Barbaro et al, 2022; Burivalova et al, 2021; Lamont et al, 2022).…”

Of acoustics and avifauna: An integrative approach to avian ecology across the Southern Hemisphere

“…Employing passive acoustic monitoring (PAM) to survey bird communities, has also driven a secondary set of objectives for this chapter. PAM has offered ecologists an effective alternative to conventional environmental or species monitoring techniques (Pijanowski et al 2011a(Pijanowski et al , 2011bGibb et al 2018;Barbaro et al 2022), with automated recording units (ARUs) making it possible to sample environments at considerable spatial and temporal scales, while enhancing the costeffectiveness of field-based research efforts (Blumstein et al 2011;Alquezar & Machado 2015;Williams et al 2018). However, managing and processing acoustic data for medium-to long-term multi-species studies still presents several challenges (i.e., time consuming), even though advances have been made to help address these (Priyadarshani et al 2018b).…”