Biodiversity scale-dependence and opposing multi-level correlations underlie differences among taxonomic, phylogenetic, and functional diversity
Mirochnitchenko, Nadejda; Stuber, Erica; Fontaine, Joseph (2021), Biodiversity scale-dependence and opposing multi-level correlations underlie differences among taxonomic, phylogenetic, and functional diversity, Dryad, Dataset, https://doi.org/10.5061/dryad.bg79cnpbb
Aim: Biodiversity is a multi-dimensional property of biological communities that represents different information depending on how it is measured, but how dimensions relate to one another and under what conditions is not well understood. We explore how taxonomic, phylogenetic, and functional diversity can differ in scale-of-effect dependence and habitat-biodiversity relationships, and subsequently how spatial differences among biodiversity dimensions may arise.
Location: Nebraska, United States
Time period: May-July 2016, 2017
Major taxa studied: Birds
Methods: Across 2016 and 2017, we conducted 2,641 point counts at 781 sites. We modeled the occupancy of 141 species using Bayesian Bernoulli-Bernoulli hierarchical logistic regressions. We calculated species richness (SR), phylogenetic diversity (PD), and functional diversity (FD) for each site and year based on predicted occupancy, accounting for imperfect detection. Using Bayesian latent indicator scale selection and multivariate modeling, we quantified the spatial scales-of-effect that best explained the relationships between environmental characteristics and SR, PD, and FD. Additionally, we decomposed the residual between- and within-site biodiversity correlations using our repeated measures design.
Results: We demonstrate spatial differences among biodiversity predictions, arising from scale-dependence in habitat-biodiversity relationships and variation in correlation structure among biodiversity dimensions. Although relationships between specific land cover types and SR, PD and FD were qualitatively similar, the spatial scales at which these variables were important in explaining biodiversity differed among dimensions. Between-site residual biodiversity correlations were negative, yet within-site biodiversity residual correlations were positive.
Main conclusions: Our results demonstrate how spatial differences among biodiversity dimensions may arise from biodiversity-specific scale-dependent habitat relationships, low shared environmental correlations and opposing residual correlations between dimensions, which suggest that single-scale and single-dimension analyses are not entirely appropriate for quantifying habitat-biodiversity relationships. After accounting for shared habitat relationships, we found positive within-site residual correlations between taxonomic, phylogenetic, and functional diversity, suggesting that habitat change over time influenced all biodiversity dimensions relatively similarly. However, negative between-site residual correlation among biodiversity dimensions may indicate trade-offs in achieving maximum biodiversity across multiple biodiversity dimensions at any given location. Although habitat management can to a limited degree improve biodiversity relatively across all metrics, other environmental effects may ensure that not all facets of biodiversity can be maximized at once. If maximizing a specific biodiversity dimension is the goal, then care should be taken to consider these within-site residual correlations.
Bird surveys: We conducted 500 m fixed radius avian point count surveys during the breeding seasons (mid-April to late-June) of 2016 and 2017, on publicly accessible secondary and tertiary roads across Nebraska, USA. Sites were grouped into ‘routes’ consisting of 7-19 point count locations such that all sites in each route could be visited within one morning. Additional routes were created in 2017 to include several of Nebraska’s Biologically Unique Landscapes, which are managed for declining rare species and unique natural communities. During surveys, trained observers recorded every bird seen or heard within a three-minute period (i.e., a visit), which occurred between 15 minutes before sunrise and 10 A.M: the time at which avian vocalizations are greatest and most consistent across species (Hutto et al., 1986). We did not perform surveys during inclement weather, including fog, drizzle, prolonged rain, and wind with speeds >20 kmh.
Land cover: Survey sites were selected based on generalized random tessellation stratified sampling, which randomly distributed sampling sites across 6 a priori-selected land cover types (based on a 30m resolution land cover product developed by the Rainwater Basin Joint Venture: Bishop et al., 2011) to spatially balance our sampling by mimicking the spatial variation across habitat gradients in Nebraska (Stevens Jr & Olsen, 2004). Using the Rainwater Basin Joint Venture land cover product, we calculated the proportions of each land cover variable within 0.5 km, 1km, 2km, 5km, 10km, 15km, and 20km radius spatial scales surrounding our sampling locations.
Functional traits: We built a functional dendrogram based on 23 functional traits: four reproductive traits, ten diet traits, one binary activity trait, one body size trait, and seven foraging strategy traits (Petchey et al., 2007). Trait information was compiled from “The Birds of North America” series from the American Ornithologists’ Society (Rodewald, 2015), the “Elton Traits 1.0” species foraging characteristics database for extant birds (Wilman et al., 2014), and the CRC Handbook of Avian Body Masses (Dunning Jr, 2007).
Location data (i.e., GPS coordinates) was not used in the analysis and thus was not included in the uploaded datasets to protect the privacy of the surrounding residents.
Nebraska Game and Parks Commission, Award: W-98-R
Nebraska Game and Parks Commission, Award: W-98-R