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Modelling the distribution of shorebirds in estuarine areas using generalised additive models Granadeiro, J.P.; Andrade, J.; Palmeirim, J.M. (2004). Modelling the distribution of shorebirds in estuarine areas using generalised additive models. J. Sea Res. 52(3): 227-240. https://dx.doi.org/10.1016/j.seares.2004.01.005 In: Journal of Sea Research. Elsevier/Netherlands Institute for Sea Research: Amsterdam; Den Burg. ISSN 1385-1101; e-ISSN 1873-1414, meer

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Beschikbaar in	Auteurs
VLIZ: Non-open access 351811 [ aanvragen ]

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Trefwoorden

Fauna > Aquatic organisms > Aquatic animals > Aquatic birds Habitat selection Modelling Portugal Topographic features > Landforms > Coastal landforms > Tidal flats Water bodies > Coastal waters > Coastal landforms > Coastal inlets > Estuaries Portugal [Marine Regions] Marien/Kust

Author keywords

estuaries; Generalised Additive Models (GAM); habitat selection;modelling; shorebirds; tidal flats

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Auteurs		Top
Granadeiro, J.P. Andrade, J. Palmeirim, J.M.

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Abstract

In this study we modelled the occurrence of the shorebirds in their intertidal feeding areas using Generalised Additive Models (GAMs). The data used for the modelling exercise consisted of regular winter counts of shorebirds in one hundred and five 50×50 m plots, arranged in a 1050×250 m area. Several physical characteristics were obtained from each plot and some other variables were derived using a Geographical Information System (GIS). These variables were used to predict the probability of occurrence of shorebirds using GAMs. The three most influential variables in the distribution of the shorebird species assemblage were the distance to channels, type of sediment and area of oyster beds. The smooth response curves of the species occurrence along the gradient of the variables were biologically meaningful, and generally consistent with previous descriptions of the habitat preferences of each species. The quality of the fits was generally very high, as assessed by the significance of the models and by comparing the observed frequencies with the predicted probabilities of occurrence. The performance of GAMs was compared with that of the equivalent GLMs, and we concluded that extended flexibility offered by GAMs resulted in better overall fits. We suggest that GAMs represent a convenient framework for modelling the large-scale distribution of shorebirds in intertidal areas from their physical characteristics.

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