In the Humber estuary, most of the spatial variability in the distribution of wader and wildfowl assemblages is ascribed to the differentiation of assemblages among the sectors within the mesohaline zone (Appendix 3). This is due to the presence of distinct assemblages in the sectors ND and NE, generally characterised by low densities of almost all the wader and wildfowl species (with the exception of Turnstone). When considering the other sectors, it is evident how the spatial variability of waders and wildfowl assemblages broadly matches with the salinity gradient in the estuary (Appendix 3). For waders, this is mainly due to the higher density of Avocet, Lapwing, Golden Plover and Black-tailed Godwit in oligohaline sectors and the higher density of all the other species (in particular Dunlin and Knot, and with the exception of Turnstone) in the polyhaline sectors. For wildfowl, this is mainly due to the higher density of Teal, Mallard, Pink-footed Goose, Canada Goose and Pintail in oligohaline sectors and the higher density of Brent Goose as well as of sea ducks (e.g. Eider, Common Scoter) in the polyhaline sectors.
The application of multivariate multiple regression models shows that a high proportion (>80%) of this observed spatial variability in the distribution of species densities in the Humber estuary can be explained by the environmental variables included in the model (Table 3). The combination of habitats coverage in the different estuarine sectors, in particular, accounts for the larger portion of this variability compared to the other types of environmental variables (including salinity, food availability (as intertidal benthic abundance) and anthropogenic disturbance)¹. The model selection process highlighted that the combination of almost all the considered variables is relevant in determining the distribution of waders and wildfowl species in the Humber, with the exception of marsh area for waders and intertidal benthic abundance for wildfowl.
When looking in detail at the importance of each environmental variable in affecting the density distribution of wader and wildfowl assemblages in the Humber estuary (as shown in Table 3² and by the graphic representation (through dbRDA³ plots) of the multivariate regression models in Figure 3), the intertidal area in the estuarine sectors results to be the predictor that can best explain the density distribution of waders (with 40% of the wader species variability explained by this variable alone). In particular, the wader assemblage differentiation that has been observed between sectors in the mesohaline zone can be mainly associated to a low availability (in terms of area) of the intertidal habitat in sectors ND and NE, leading to the scarce presence of most waders in these areas. This is associated with a higher occurrence of hard substrata (pebbly areas and man-made structures), a likely responsible for the higher density of Turnstone in these sectors, due to its habit of feeding on hard substratum cobbles and weed. In turn, the area of the intertidal habitat in the sectors is positively correlated with the distribution of most of the species occurring with higher density in the outer estuary (e.g. Knot, Dunlin, Bar-tailed Godwit) (Table 4). Supralittoral area is the weakest predictor of waders density distribution among those included in the model (Table 3).
When considering the wildfowl assemblage, the best predictor of its distribution in the Humber is the marsh area, this variable alone accounting for 26% of the species density variability. In general, higher density of most wildfowl species (in all sectors except for ND and NE) are associated to a higher availability of marsh habitat (in terms of coverage area) in the sector (Table 4, Figure 3). In turn, anthropogenic disturbance and subtidal area are the weakest predictors of wildfowl density distribution among those included in the model for the Humber (Table 3).
¹ Although this result might be influenced also by the higher number of habitat variables included in the analysis compared to the number of the other variables.
² In particular, single predictor models (i.e., regression models relating the distribution of the species densities to one variable at a time) can be used to rank the importance of each environmental variable in affecting the bird assemblage distribution.
³ Distance-based Redundancy Analysis (Legendre and Anderson 1999)
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