DOI: 10.5061/dryad.ngf1vhhx7
title: Seeing shapes in clouds: the fallacy of deriving ecological hypotheses from statistical distributions

description: The explanations behind observations of global patterning in species
 diversity pre-date the field of ecology itself. The generation of new
 species-area theories, in particular, far outpaces their falsification,
 resulting in a centuries-old accumulation in species diversity theories.
 We use historical assessment and new data analysis to argue that one of
 the earliest recognized and most consistent patterns in species diversity
 is not strictly an ecological phenomenon and, when ecological mechanism is
 invoked, the range of potential mechanisms is too numerous for tractable
 hypothesis falsification. We provide a historical parallel in that the
 normal distribution once was treated as a pattern assuming a biological
 mechanism rather than a statistical distribution that can be generated by
 biological and non-biological forces. Similarly, power law distributions
 are ubiquitous in aggregated data, such as the species-area relationship.
 That nearly identical broad-scale aggregation patterns are observed for
 both ecological and non-ecological data as a function of area suggest that
 these broad-scale patterns reflect a statistical distribution that, in
 itself, cannot be used to discern between or among ecological and
 non-ecological mechanisms. We argue that by seeking processes in such a
 ubiquitous pattern, ecologists may read ecological mechanism into
 statistical patterns, and we suggest that falsifying broad-scale diversity
 distribution hypotheses should be a greater priority than generating or
 parameterizing new ones.
description: We used worldwide nationality boundaries to aggregate data by
 standard boundaries that are comparable across disparate data sets (and
 because publicly available species and non-species data commonly are
 aggregated by national boundaries). We chose species data sets where
 abundances were available by country (amphibians, ants, birds, freshwater
 fish, human diseases, Lepidopteran species, mammals, mosquitoes,
 non-natives, rare, reptiles, trees, crops). We chose non-species data sets
 that parallel species (where individuals are aggregated by their group
 membership) [airlines, cheeses, cities, colleges, hospitals, imports,
 industries, journals, livestock, newspaper companies, political parties].
 For example, the number of newspapers printed in a country would not be
 parallel, but the number of newspaper companies printing newspapers is
 parallel to the number of species containing individual organisms. We used
 several data sources where ecological and non-ecological data were
 aggregated by the same country boundaries: ·      
 wikipedia.org ·       www.worldatlas.com
 ·       www.nationmaster.com ·      
 www.ebird.org ·       www.iucnredlist.org
 ·       fish.mongabay.com/biotope_countries.htm
 ·      
 bovbeers.wordpress.com/2011/07/02/number-of-breweries-worldwide
 ·       www.scimagojr.com/journalrank.php
 ·       www.iucnredlist.org ·      
 www.antwiki.org/wiki/Diversity_by_Country ·      
 vectormap.si.edu/MosquitoCountryList.htm ·      
 reptile-database.reptarium.cz ·       www.invasive.org/species.cfm.