Biology subjects

Gerstner, K.

Publications and source records attributed to Gerstner, K..

2 recordsLinked to original sources

bioRxiv2019

A cross-scale assessment of productivity-diversity relationships

AimBiodiversity and ecosystem productivity vary across the globe and considerable effort has been made to describe their relationships. Biodiversity-ecosystem functioning research has traditionally focused on how experimentally controlled species richness affects net primary productivity (S[->]NPP) at small spatial grains. In contrast, the influence of productivity on richness (NPP[->]S) has been explored at many grains in naturally assembled communities. Mismatches in spatial scale between approaches have fostered debate about the strength and direction of biodiversity-productivity relationships. Here we examine the direction and strength of productivitys influence on diversity (NPP[->]S) and of diversitys influence on productivity (S[->]NPP), and how this varies across spatial grains. Locationcontiguous USA Time period1999 - 2015 Major taxa studiedwoody species (angiosperms and gymnosperms) MethodsUsing data from North American forests at grains from local (672 m2) to coarse spatial units (median area = 35,677 km2), we assess relationships between diversity and productivity using structural equation and random forest models, while accounting for variation in climate, environmental heterogeneity, management, and forest age. ResultsWe show that relationships between S and NPP strengthen with spatial grain. Within each grain, S[->]NPP and NPP[->]S have similar magnitudes, meaning that processes underlying S[->]NPP and NPP[->]S either operate simultaneously, or that one of them is real and the other is an artifact. At all spatial grains, S was one of the weakest predictors of forest productivity, which was largely driven by biomass, temperature, and forest management and age. Main conclusionsWe conclude that spatial grain mediates relationships between biodiversity and productivity in real-world ecosystems and that results supporting predictions from each approach (NPP[->]S and S[->]NPP) serve as an impetus for future studies testing underlying mechanisms. Productivity-diversity relationships emerge at multiple spatial grains, which should widen the focus of national and global policy and research to larger spatial grains.

ecology↗

bioRxiv2017

mobsim: An R package for the simulation and measurement of biodiversity across spatial scales

1. Estimating biodiversity and its changes in space and time poses serious methodological challenges. First, there has been a long debate on how to quantify biodiversity, and second, measurements of biodiversity change are scale-dependent. Therefore comparisons of biodiversity metrics between communities are ideally carried out across scales. Simulation can be used to study the utility of biodiversity metrics across scales, but most approaches are system specific and plagued by large parameter spaces and therefore cumbersome to use and interpret. However, realistic spatial biodiversity patterns can be generated without reference to ecological processes, which suggests a simple simulation framework could provide an important tool for ecologists.\n\n2. Here, we present the R package mobsim that allows users to simulate the abundances and the spatial distribution of individuals of different species. Users can define key properties of communities, including the total numbers of individuals and species, the relative abundance distribution, and the degree of spatial aggregation. Furthermore, the package provides functions that derive biodiversity patterns from simulated communities, or from observed data, as well as functions that simulate different sampling designs.\n\n3. We show several example applications of the package. First, we illustrate how species rarefaction and accumulation curves can be used to disentangle changes in the fundamental biodiversity components: (i) total abundance, (ii) relative abundance distribution, (iii) and species aggregation. Second, we demonstrate how mobsim can be used to assess the performance of species-richness estimators. The latter indicates how spatial aggregation challenges classical non-spatial species-richness estimators.\n\n4. mobsim allows the simulation and analysis of a large range of biodiversity scenarios and sampling designs in an efficient and comprehensive way. The simplicity and control provided by the package can also make it a useful didactic tool. The combination of controlled simulations and their analysis will facilitate a more rigorous interpretation of real world data that exhibit sampling effects and scale-dependence.

ecology↗