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. 2. 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. 3. 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. 4. 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.

[1]  David Kenfack,et al.  A general framework for the distance–decay of similarity in ecological communities , 2008, Ecology letters.

[2]  Duccio Rocchini,et al.  Spatially constrained rarefaction : incorporating the autocorrelated structure of biological communities into sample-based rarefaction , 2009 .

[3]  Anne Chao,et al.  Unifying Species Diversity, Phylogenetic Diversity, Functional Diversity, and Related Similarity and Differentiation Measures Through Hill Numbers , 2014 .

[4]  M. Heard.  Using a Problem-Based Learning Approach to Teach Students About Biodiversity, Species Distributions & the Impact of Habitat Loss , 2016, The American Biology Teacher.

[5]  Michael S. Rosenberg,et al.  Handbook of spatial point-pattern analysis in ecology , 2015, Int. J. Geogr. Inf. Sci..

[6]  Robert K. Colwell,et al.  Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness , 2001 .

[7]  Katja Gruenewald,et al.  Species Diversity In Space And Time , 2016 .

[8]  C. Rahbek.  The role of spatial scale and the perception of large‐scale species‐richness patterns , 2004 .

[9]  R. Salomão,et al.  Estimating species richness in hyper-diverse large tree communities. , 2017, Ecology.

[10]  Brian J McGill,et al.  Linking biodiversity patterns by autocorrelated random sampling. , 2011, American journal of botany.

[11]  Anne E. Magurran,et al.  Biological Diversity: Frontiers in Measurement and Assessment , 2011 .

[12]  Robert K. Colwell,et al.  Estimating terrestrial biodiversity through extrapolation. , 1994, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[13]  A. Chao.  Estimating the population size for capture-recapture data with unequal catchability. , 1987, Biometrics.

[14]  L. Jost.  Entropy and diversity , 2006 .

[15]  Jonathan M. Chase,et al.  Scale-dependent effect sizes of ecological drivers on biodiversity: why standardised sampling is not enough. , 2013, Ecology letters.

[16]  A. Chao,et al.  An improved nonparametric lower bound of species richness via a modified good–turing frequency formula , 2014, Biometrics.

[17]  Pierre Legendre,et al.  SPECIES DIVERSITY PATTERNS DERIVED FROM SPECIES–AREA MODELS , 2002 .

[18]  P. White,et al.  The distance decay of similarity in biogeography and ecology , 1999 .

[19]  A. Kinzig,et al.  On the implications of species-area relationships for endemism, spatial turnover, and food web patterns , 1997 .

[20]  B. McGill.  Towards a unification of unified theories of biodiversity. , 2010, Ecology letters.

[21]  Robert K. Colwell,et al.  Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages , 2012 .