Project part-financed by the European Union (European Regional Development Fund)

The Interreg IVB North Sea Region Programme

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Determinants of bird habitat use in TIDE estuaries

4. Data used

Bird data were analysed for the three estuaries in combination with data on environmental characteristics (habitat area and quality, water quality parameters, disturbance indicator) in order to describe the species distribution within the estuarine areas and to identify the main environmental determinants of their habitat use. Multivariate regression models were applied to investigate the whole bird assemblage distribution (distinguishing between waders and wildfowl), whereas univariate regression models were calibrated to identify the main predictors of single species distribution within the estuary. Similar analyses were carried out in the three estuaries, in order to identify common patterns and elements of differentiations due to the local conditions.

This chapter provides an overview of the type of data that were used in this study, with further details and examples on how the data were derived being provided in Appendix 1

The annual maximum counts for wader and wildfowl species in estuarine spatial units at high-tide were analysed. The main focus of the analysis was on the spatial distribution of bird species, but also temporal variability was accounted for by including data collected in different years.

In the Humber, data for 11 units (WeBS sectors) covering the North bank of the estuary (Figure 1A) were available between 1991 and 2011 for waders and between 1975 and 2011 for wildfowl (WeBS national survey). In the Elbe, data for 59 units along the southern bank (Niedersachsen jurisdiction, NDS) and 19 units along the northern bank (Schleswig-Holstein jurisdiction, SH) (Figure 1B) were available between 1984 and 2011 for both waders and wildfowl (Joint Monitoring of Migratory Birds, JMMB). In the Weser, data for 82 units along the estuary (both banks) (Figure 1C) were available between 1984 and 2009 (Joint Monitoring of Migratory Birds, JMMB). In order to allow comparison between units of different size, count data were standardised to densities (ind/km2) before any analysis, based on the area of each unit.

The spatial-temporal distribution of bird assemblages and species was related to a set of environmental variables describing the habitat characteristics, water quality and anthropogenic disturbance in each counting unit, sector or estuarine zone in different years. The environmental variables included in the analyses as possible predictors of bird habitat use are listed in Table 1.

Habitat coverage data in each unit/sector were calculated from historical maps available for the studied estuaries (details on the method used and an example of this calculation are reported in Appendix 1a). As a result, annual habitat coverage data for each unit/sectors were obtained from 1975 to 2011 in the Humber, 1984 to 1998 in the Elbe, and 1984 to 2003 in the Weser. For the Humber only, the intertidal habitat in the studied units was characterised also in a qualitative way through the identification of the dominant intertidal habitat type and the coverage of hard substrata (either pebbles or man-made vertical substratum) present within each sector (see Appendix 1b and 1c for details).

As regards water quality data, the average salinity in each sector in the Humber Estuary was calculated based on different sources (Gameson 1982, Falconer & Lin 1997, Humber salinity zonation 2000-2010; spatial variability was only considered, with the same salinity allocated to each sector in different years). For the Elbe and Weser, chlorinity was considered as an indicator of the salinity gradient and the data were obtained from the dataset used for the report on an inter-estuarine comparison for ecology in TIDE . Additional water quality data were derived for the Elbe and Weser from this dataset, based on their suitability as possible predictors of bird habitat use, their level of inter-correlation and in order to maximise the coverage in the selected dataset. In particular, eutrophication (in terms of changing nutrient inputs) is considered one of the main processes influencing the quality and the stocks of benthic prey for birds in the Wadden Sea, and total phosphate (PO4), summer chlorophyll and autumn NH4 and NO2 have been regarded as good indicators of the eutrophication status in this area (Ens et al. 2009). Also Biochemical Oxygen Demand (BOD), an indicator of organic and nutrient loading influences, and dissolved oxygen saturation (DOsat) have been used as predictors of bird distribution in estuarine and coastal areas (Burton et al. 2002). It is of note that, in the available dataset for the Weser and the Elbe, summer chlorophyll data were sparse therefore they were not included in the analysis. In addition, this variable was highly correlated (Spearman correlation coefficient rS>0.8) to BOD (positive correlation) and to chlorinity (negative correlation) in the Elbe estuary. In this estuary, autumn NH4 only was considered, being highly positively correlated (rS>0.9) with autumn NH4 + NO2 values. As regards the Weser, DOsat was not considered, due to the limited availability for this variable in the dataset and its negative correlation (rS>0.6) with chlorinity. In the water quality dataset, data were available seasonally from 2004 to 2009 in the Elbe, and 1992 to 2009 in the Weser and by wider estuarine zones (see report on Zonation of the TIDE estuaries ), therefore units located within the same zone were given the same value (annual average values were calculated when seasonal values were not explicitly required). It is of note that, in the Weser, no water quality data were available for the mesohaline and polyhaline zones.

The quality of the intertidal habitat, in terms of provision of food resources to bird species, was also measured for each sector in the Humber Estuary based on the information provided in Allen (2006). In particular, the total benthic invertebrate abundance was considered as an estimate of the total amount of food potentially available to wading birds within each sector. Also the type of benthic community (based on its species composition and density) characterising the intertidal habitat in each sector was considered as a possible relevant factor in affecting bird use by accounting for the quality of the food resource potentially available together with its quantity. Further details on these aspects are reported in Appendix 1d.

For the Humber Estuary, an index of the frequency of potentially disturbing activities in the sectors was also calculated based on data provided in Cruickshanks et al. (2010). This variable was called Disturbance (see details in Appendix 1e). No such detailed data were available for the different spatial units in the Weser and Elbe. However, it is of note that a differentiation between the northern and southern bank occur within the same estuarine zone in the Elbe estuary, due to the different distribution of natural areas and areas of anthropogenic influence (e.g. industrial estates, infrastructures) in the two banks subject to different jurisdictions (Niedersachsen (NDS) for the southern bank, Schleswig-Holstein (SH) for the northern bank). Therefore, the two different jurisdictions were included in the analysis for the Elbe estuary as a factor that might possibly have an effect on bird use of estuarine habitats.

Although the main focus of the study was on the spatial distribution and habitat use, temporal variables were also included in the analysis in order to take account of this source of variability in the data. Year was considered in the species distribution models, as well as the wider species population trend. For the Humber estuary, data on annual total maximum counts for Great Britain (1975 to 2011) were collected for selected species from WeBS books (details on this type of data are provided in Appendix 1f). For the Elbe and Weser Estuary, estimates of the population size in the Niedersachsen area only (for the Weser) and also in the Schleswig-Holstein area (for the Elbe) were derived from the population trends (between 1987 and 2008) analysed in Laursen et al. 2011 (detailed methods can be found also in Blew et al. 2005, 2007).

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