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Implications of movement for species distribution models - rethinking environmental data tools
Bruneel, S.; Gobeyn, S.; Verhelst, P.; Reubens, J.; Moens, T.; Goethals, P. (2018). Implications of movement for species distribution models - rethinking environmental data tools. Sci. Total Environ. 628-629: 893-905. https://hdl.handle.net/10.1016/j.scitotenv.2018.02.026
In: Science of the Total Environment. Elsevier: Amsterdam. ISSN 0048-9697; e-ISSN 1879-1026, meer
Peer reviewed article  

Beschikbaar in  Auteurs 

Trefwoord
    Measurement > Telemetry
Author keywords
    Species distributions; Fish movement; Environmental data collection

Auteurs  Top 
  • Bruneel, S.
  • Gobeyn, S.
  • Verhelst, P.
  • Reubens, J.
  • Moens, T.
  • Goethals, P.

Abstract
    Movement is considered an essential process in shaping the distributions of species. Nevertheless, most species distribution models (SDMs) still focus solely on environment-species relationships to predict the occurrence of species. Furthermore, the currently used indirect estimates of movement allow to assess habitat accessibility, but do not provide an accurate description of movement. Better proxies of movement are needed to assess the dispersal potential of individual species and to gain a more practical insight in the interconnectivity of communities. Telemetry techniques are rapidly evolving and highly capable to provide explicit descriptions of movement, but their usefulness for SDMs will mainly depend on the ability of these models to deal with hitherto unconsidered ecological processes. More specifically, the integration of movement is likely to affect the environmental data requirements as the connection between environmental and biological data is crucial to provide reliable results. Mobility implies the occupancy of a continuum of space, hence an adequate representation of both geographical and environmental space is paramount to study mobile species distributions. In this context, environmental models, remote sensing techniques and animal-borne environmental sensors are discussed as potential techniques to obtain suitable environmental data. In order to provide an in-depth review of the aforementioned methods, we have chosen to use the modelling of fish distributions as a case study. The high mobility of fish and the often highly variable nature of the aquatic environment generally complicate model development, making it an adequate subject for research. Furthermore, insight into the distribution of fish is of great interest for fish stock assessments and water management worldwide, underlining its practical relevance.

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