Michael T. Burrows (1), Jorge García Molinos (1,2), Benjamin S. Halpern, B (3,4,5) , David S. Schoeman (6), Nova Mieszkowska (7), Stephen J. Hawkins (8), Martin Edwards (9,10), Elvira S. Poloczanska (11,12)
1 Department of Ecology, Scottish Association for Marine Science, Scottish Marine Institute, Oban, Argyll, PA37 1QA, Scotland, UK
2 Center for Environmental Biology and Ecosystem Studies, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan.
3 Bren School of Environmental Science and Management, University of California, Santa Barbara, California 93106, USA
4 Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot SL5 7PY, UK
5 NCEAS, 735 State St., Santa Barbara, California 93101, USA
6 School of Science and Engineering, University of the Sunshine Coast, Maroochydore, Queensland 4558, Australia
7 The Laboratory, Marine Biological Association of the United Kingdom, Citadel Hill, Plymouth, UK
8 Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Southampton, UK
9 Sir Alister Hardy Foundation for Ocean Science, The Laboratory, Citadel Hill, Plymouth PL1 2PB, UK.
10 Marine Institute, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
11The Global Change Institute, The University of Queensland, Brisbane, Queensland 4072, Australia.
12 CSIRO Oceans and Atmosphere Flagship, Ecosciences Precinct, Boggo Road, Brisbane, Queensland 4001, Australia
Multiple range shifts by individual species combined with changes in abundance of climate-sensitive ones produce changes in the structure of biological communities. Here we explore the use of the Community Temperature Index (CTI), a measure of the average temperature preference of all the species present weighted by their relative abundance, to reflect climate-induced change at the assemblage level. We use community data that are both spatially extensive and repeated in time to show how communities from different marine regions and habitats are more or less sensitive to climate change over different time lags and spatial scales. For example, two rocky shore datasets showed strong responses as measured by the slope of CTI versus sea surface temperature (SST), and positive correlations between CTI and SST up to a 4-5 year lag. Contrasting responses are expected from communities dominated by long-lived species, with weaker dependence between CTI and environmental temperature, and more rapid, shorter-lagged responses from communities dominated by short-lived species, such as plankton, or highly mobile species, such as fish. Characteristic global patterns in marine CTIs emerge from synthetic species distributions (17000+Aquamaps species). CTI based on oceanic species changes less quickly with latitude than for coastal species, largely due to the smaller range sizes of the latter. Finally changes in CTI emerge from models that predict the effects of global change on patterns of biodiversity from predicted range shifts. Patterns of predicted change in CTI identify particular areas where rapid community level shifts may occur.