posted on 2023-05-30, 05:03authored byJian DL Yen, James R Thomson, Jonathan M Keith, David M Paganin, Erica Fleishman, Andrew BennettAndrew Bennett, Dale G Nimmo, Joanne M Bennett, David S Dobkin, Ralph MacNally
The rapid development of mechanistic, trait-based models has resulted in increasingly reliable predictions of the functional diversity of individuals in populations and communities. However, a focus on individuals’ traits differs from the prevailing focus on species in much of community ecology. We sought to identify correlative links between species richness and size diversity, focusing on size diversity as one component of functional diversity. These links could be used to extend individual, size-based models to predict patterns of species richness. We used the distribution of the sizes of individuals in a community – the individual–size distribution (ISD) – as a measure of size diversity, and constructed Bayesian regression models with species richness as the response variable and ISDs as the predictor variables. We used two methods to include ISDs in our analyses. First, we summarized the ISD with five common diversity indices and used these indices as predictor variables in our analyses. Second, we used functional data analysis to include the entire ISD (a continuous function) as a predictor variable in our analyses. Analyses of diversity indices identified consistent, positive associations between species richness and size diversity. Analyses of entire ISDs revealed that these associations were driven by numbers of small- and medium-sized individuals. In general, a combination of diversity indices predicted species richness as well as or better than continuous ISDs. However, models with ISDs as predictor variables were less sensitive to technical details of model fitting (e.g. discretization method) than those based on diversity indices, and the use of ISDs avoids the arbitrary selection of one or several diversity indices. Our use of functional data analysis allows any trait distribution to be included as a variable in statistical analyses, and has the potential to reveal new diversity patterns in ecology.
Funding
This research was conducted by the Australian Research Council Centre of Excellence for Environmental Decisions (CE11001000104) and was funded by the Australian Government. Data on fishes were available due to the efforts of the many researchers involved in the NAWQA program (USGS). Australian bird-mass data were collated by C. Catterall, R. Loyn, and T. Sloane, with support from the Arthur Rylah Institute for Environmental Research. Funds for collection and archiving of data on birds in the Great Basin were provided by the Joint Fire Science Program via cooperative agreements with the Rocky Mountain Research Station (JFSP 00-2-15, 01B-3-3-01, 05-2-1-94, and 09-1-08-4), by the National Fish and Wildlife Foundation (2005-0294-000), and by the Strategic Environmental Research and Development Program of the Department of Defense (contract W912HQ-12-C-0033, project RC-2202). JDLY was funded by a Monash University Sir James McNeill Foundation Postgraduate Research Scholarship and Monash University Postgraduate Publications Award and received financial support from the Victorian Life Sciences Computation Initiative.