Consequences of model assumptions when projecting habitat suitability: a caution of forecasting under uncertainties. (8th June 2021)

• Record Type: Journal Article
• Authors: Hodgdon, Cameron T; Mazur, Mackenzie D; Friedland, Kevin D; Willse, Nathan; Chen, Yong
• Editors: Selden, Rebecca
• Is Part Of: ICES journal of marine science. Volume 78:Number 6(2021)
• Page Start: 2092
• Page End: 2108
• Publication Date: 2021-06-08
• Subjects: bioclimate modelling -- data input assumptions -- data uncertainty -- habitat suitability index -- spatial forecasting; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsab101
• Languages: English
• ISSNs: 1054-3139

Abstract

Abstract: Climate change is continuing to influence spatial shifts of many marine species by causing changes to their respective habitats. Habitat suitability as a function of changing environmental parameters is a common method of mapping these changes in habitat over time. The types of models used for this process (e.g. bioclimate models) can be used for projecting habitat if appropriate forecasted environmental data are used. However, the input data for this process must be carefully selected as less reliable results can incite mis-management. Thus, a knowledge of the organism and its environment must be known a priori . This paper demonstrates that these assumptions about a species' life history and the environment are critical when applying certain types of bioclimate models that utilize habitat suitability indices. Inappropriate assumptions can lead to model results that are not representative of environmental and biological realities. Using American lobster ( Homarus americanus ) of the Gulf of Maine as a case study, it is shown that the choice of extrapolation data, spatial scale, environmental parameters, and appropriate subsetting of the population based on life history are all key factors in determining appropriate biological realism necessary for robust bioclimate model results.