کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4549956 | 1627496 | 2013 | 10 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Modelling spatial distribution of epibenthic communities in the Gulf of St. Lawrence (Canada) Modelling spatial distribution of epibenthic communities in the Gulf of St. Lawrence (Canada)](/preview/png/4549956.png)
Correlative habitat models using relationships between marine organisms and their surrounding environment can be used to predict species distribution, and the results can assist management of human activities sharing the marine space (e.g. fisheries, MPAs, tourism). Here, epi-benthic megafauna was sampled at 755 stations in the Lower Estuary and Northern Gulf of St. Lawrence (EGSL) each summer between 2006 and 2009. We combined various types of multivariate analyses to 1) describe the structure and spatial distribution of benthic communities, 2) analyse the relationship between these communities and environmental parameters, and subsequently 3) build a community distribution model to predict the spatial distribution of the communities, creating community distribution maps covering the entire area to be used for marine management and conservation. We identified distinct benthic communities in the study area that closely correlate with the 200 m depth contour and with major environmental variables. A redundancy analysis revealed that communities were associated with depth, oxygen saturation, temperature, bottom current, seabed uniformity, distance to coast and type of sediment. Together these environmental descriptors explained 38% of the variation in megafaunal community composition. The environmental variables were used to build a community distribution model using generalized linear models to predict high and low suitability zones of each community in the EGSL.
► Epibenthic megafaunal communities of the Gulf of St. Lawrence were described.
► Their relationships with the environmental conditions were assessed.
► A statistical community distribution model was designed.
► Communities are strongly related to their environment.
► The different communities are predicted to live in specific habitats.
Journal: Journal of Sea Research - Volume 78, April 2013, Pages 75–84