Ambient, productive and wind energy, and ocean extent predict global species richness of procellariiform seabirds

• Published in: Global Ecology and Biogeography
• Main Authors: Davies, Richard G., Irlich, Ulrike M., Chown, Steven L., Gaston, Kevin J.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2009
• Subjects: Southern Ocean
• Online Access: http://dx.doi.org/10.1111/j.1466-8238.2009.00498.x; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.1466-8238.2009.00498.x; https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1466-8238.2009.00498.x

ABSTRACT Aims Tests of the energy hypothesis for the large‐scale distribution of species richness have largely been concerned with the influence of two alternative forms of environmental energy, temperature and energy from primary productivity, both of which (at least in terrestrial systems) peak within the tropics. Taxa showing extra‐tropical diversity peaks present a potential challenge to the generality of species–energy theory. One such group are pelagic seabirds of the order Procellariiformes that show not only an extra‐tropical diversity peak but one confined to the Southern Ocean, hence a highly asymmetric one. They are distinct in being exceptionally adapted to take advantage of wind energy, which they may rely on for long‐distance ocean foraging for the patchy resources needed to meet their energetic needs. Wind represents a readily available source of kinetic energy, shows a strong latitudinal gradient, and has been largely omitted from species–energy theory. Moreover, maximal benefits of wind are likely to be afforded in areas of greatest available contiguous ocean extent. We compare the relative importance of wind speed, ocean productivity (chlorophyll concentration), air temperature and available ocean extent (distance) in explaining large‐scale global distribution of procellariiform species richness across the world's oceans. Location Global, oceanic. Methods Hierarchical partitioning, model selection, ordinary least squares (OLS) and spatial generalized least squares (GLS) regression. Results Hierarchical partitioning of non‐spatial regression models indicates that ocean distance is the most important predictor of procellariiform species richness followed by wind speed and then temperature. In contrast, that of spatial regression models indicates the roughly equal importance of ocean distance and temperature, followed by wind speed. Although contributing additional model fit, ocean productivity is consistently the weakest predictor. Best‐fit models include all four predictors and explain 67% of ...