The spatial dimension of marine bird data and its implication for biodiversity monitoring. Ainārs Auniņš Latvian Fund for Nature
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1 The spatial dimension of marine bird data and its implication for biodiversity monitoring Ainārs Auniņš Latvian Fund for Nature
2 GES criteria Descriptor 1: Biological diversity is maintained. The quality and occurrence of habitats and the distribution and abundance of species are in line with prevailing physiographic, geographic and climate conditions Species level Species distribution Distributional range Distributional pattern within the range Area covered by species Population size Abundance and/or biomass Population condition Demographic characteristics Genetic structure Assessment required at 3 ecological levels: Ecosystem Habitats Species
3 Abundance and distribution indicators (wintering season) MARMONI Abundance index of wintering waterbird species Wintering waterbird index Wintering indices for waterbirds of different feeding guilds Distribution of wintering waterbird species Distribution of wintering waterbirds (multi-species) Distribution of wintering waterbirds of different feeding guilds CORESET Abundance of waterbirds in the wintering season
4 Single species abundance indicators (Gulf of Riga) International waterbird counts Seacoast data only Yearly abundance index
5 Indicator development TRIM Estimation of missing data (generalised estimating equations) Indices and trends: log-linear regression Serial correlation: ON Annual indices & SE Virtual base year: mean of
6 Goldeneye Bucephala clangula Index *SE ± MI**
7 Mallard Anas platyrhynchos Index *SE ± Stable
8 Long-tailed Duck Clangula hyemalis Index *SE ± MI**
9 Species trends Species Trend Species Trend Benthos feeders Velvet Scoter M. fusca Common Scoter M. nigra Long-tailed Duck C. hyemalis Common Eider S. mollissima Steller s Eider P. stelleri ± U ± U ± MI** ± U ± U Plant eaters Mallard A. platyrhynchos Mute Swan C. olor Coot F. atra Fish eaters Red-throated Diver G. stellata ± S ± S ± U ± U Tufted Duck A. fuligula Greater Scaup A. marila Goldeneye B. clangula Others Pochard A. ferina Smew M. albellus ± U ± U ± MI** ± U ± SI** Black-throated Diver G. arctica Great Crested Grebe P. cristatus Goosander M. merganser Red-breasted merganser M. serrator Cormorant Ph. carbo ± U ± U ± S ± S ± U SI strong increase S stable MI moderate increase U - uncertain
10 Abundance indicators Trends uncertain for most species The waterbirds in the Gulf of Riga belong to a wider population Populations are mobile = able to change distribution due to changing ice conditions Affected by Local ice conditions Ice conditions further north Need for a wider assessment Need to account for climate effects
11 Single species abundance indicators (Baltic Sea) International waterbird counts Seacoast data only Yearly abundance index
12 Sampling
13 Weighting Different sampling intensity in different parts of the Baltic Weighting used for compensating undersampling in some parts of the coastline 1km wide coastal buffer target area Proportion of sampled area calculated for each country and subbasin
14 Weigthing Country Subbasins Sampling sites % area sampled Denmark Estonia Germany Latvia Poland Sweden Finland Lithuania Russia sites
15 Indicator development TRIM Estimation of missing data (generalised estimating equations) Trends: log-linear regression Covariate (factor): subbasins Serial correlation: ON Weighting: ON Annual indices & SE Virtual base year: mean of GAM Tested as a response to criticism TRIM does not allow for scalar covariates No bootstrapping implemented Calculation: GAMM Inclusion of covariates: temperature Factor: subbasins Serial correlation: ON Weighting: ON Bootstrapping confidence intervals
16 Covariates: temperature Affects distribution of wintering birds E-OBS gridded dataset ver. 7.0 (NetCDF) 0.25 deg. regular grid daily mean temperature 3 datasets calculated for each year ( ) mean T of the 1st half of January mean T 2 weeks before the central dates of the IWC mean T 1 week before the central dates of the IWC Value for each sampling location extracted from matching (or nearest) grid cell
17 Covariates: temperature
18 Impact of temperature: examples Significant effect for most species Great Crested Grebe Great Cormorant Common Scoter Common Goldeneye Mute Swan
19 Goldeneye Bucephala clangula Index *SE TRIM ± MI**
20 Goldeneye Bucephala clangula Index *SE Index + 95% CI TRIM GAM ± MI**
21 Mallard Anas platyrhynchos Index *SE TRIM ± MD*
22 Mallard Anas platyrhynchos Index *SE Index + 95% CI TRIM GAM ± MD*
23 Long-tailed Duck Clangula hyemalis Index *SE TRIM ± MD**
24 Long-tailed Duck Clangula hyemalis Index *SE TRIM GAM ± MD** Index + 95% CI
25 Abundance indicators Inclusion of temperature in models has significant impact on the obtained indices and their trajectories Inclusion of other site specific covariates might be useful Impact of global factors (e.g. NAO) might be considered
26 Limitations of «coastal» data Only coastal/inshore data coverage No regular abundance samples available from offshore areas Indicators calculated from IWC data reliable only for coastal species Coastal counts cannot reflect population change offshore Offshore bird monitoring is needed
27 Offshore bird surveys Winter 2012 Winter 2014
28 Goldeneye Bucephala clangula r S = 0.75, p < r S = 0. 74, p < 0.001
29 Long-tailed Duck Clangula hyemalis r S = , p = r S = , p =
30 Long-tailed Duck Clangula hyemalis Winter 2012 Distance + GAM
31 Spatial distribution models Abundance/density value in every grid cell Change of abundance/density in every grid cell between two survey events Spatially explicit abundance indices Spatially explicit population trends Spatially explicit multispecies indices and trends
32 Long-tailed Duck Clangula hyemalis Winter 2012 Winter 2014
33 Long-tailed Duck Clangula hyemalis Change: Winter 2014 Winter 2012
34 Conclusions and recommendations Offshore bird monitoring is essential to get reliable abundance and distribution data of marine bird species Offshore monitoring needs to be synchronised among countries Combination of ground counts and plane (or ship) counts is preferred to ensure good coverage of different species Baltic Sea wide assessments are important for species with wide distribution areas Covariate(s) describing climatic factors need to be considered in indicator calculation
35 Contributors and data providers Gulf of Riga: Andres Kuresoo, Leho Luigujõe, Estonian University of Life Sciences, Antra Stīpniece, Latvian Ornithological society Baltic Sea (CORESET): Preben Clausen, Department of Bioscience, Aarhus University, Mindaugas Dagys, Laboratory of Avian Ecology, Nature Research Centre, Stefan Garthe, Research and Technology Centre, University of Kiel, Gennady Grishanov, Department of Ecology and Zoology, Immanuel Kant Baltic federal University, Samuli Korpinen, Helsinki Commission, Andres Kuresoo, Estonian University of Life Sciences, Aleksi Lehikoinen, Finnish Museum of Natural History, University of Helsinki, Leho Luigujõe, Estonian University of Life Sciences, Włodzimierz Meissner, Department of Vertebrate Ecology and Zoology, University of Gdansk, Markku Mikkola-Roos, Finnish Environment Institute, Leif Nilsson, Department of Biology, Lund University, Ib Krag Petersen, Department of Bioscience, Aarhus University, Antra Stīpniece, Latvian Ornithological society, Johannes Wahl, Dachverband Deutscher Avifaunisten
36 Thank you! Thanks to many volunteers collecting the data
37 Multi-species indices Choice of measure: species richness, diversity index (Shannon, Simpson, modified Shannon,...), percentage increasing percentage declining, total abundance index of all species,, arithmetic mean of abundance indices, geometric mean of abundance indices Geometric mean of abundance indices Every species treated equally Choice of species set
38 Calculation of multi-species index Geometric mean of species abundance indices Every species treated equally Calculation of variance (and standard errors) Ī multi-species index value T number of indices (species) I t species abundance index value Gregory R.D., van Strien A.J., Vorisek P., Gmelig Meyling A.W., Noble D.G., Foppen R.P.B. et Gibbons D.W. (2005): Developing indicators for European birds. Philosophical Transactions of the Royal Society B 360:
39 Wintering waterbird index TRIM GAM Included species (19): Gavia stellata, Gavia arctica, Podiceps cristatus, Phalacrocorax carbo, Cygnus olor, Anas platyrhynchos, Aythya ferina, Aythya fuligula, Aythya marila, Somateria mollissima, Polysticta stelleri, Clangula hyemalis, Melanitta nigra, Melanitta fusca, Bucephala clangula, Mergus merganser, Mergus serrator, Mergus albellus, Fulica atra Gulls not included due to enormous variation of yearly indices and SE values exceeding index values
40 Feeding niche indices: piscivores TRIM GAM Included species (6): Gavia stellata, Gavia arctica, Podiceps cristatus, Phalacrocorax carbo, Mergus merganser, Mergus serrator
41 Feeding niche indices: herbivores TRIM GAM Included species (3): Cygnus olor, Anas platyrhynchos, Fulica atra
42 Feeding niche indices: benthic feeders TRIM GAM Included species (9): Aythya ferina, Aythya fuligula, Aythya marila, Somateria mollissima, Polysticta stelleri, Clangula hyemalis, Melanitta nigra, Melanitta fusca, Bucephala clangula
43 Limitations Only coastal/inshore data coverage Do not misinterpret these population indices as representing populations of the whole Baltic sea for the species with offshore distribution they represent coastal/inshore populations only No yearly abundance samples available from offshore areas Current availability of offshore data Only some regions of the Baltic sea Only few points in time series, national efforts not synchronised Different data collection methods (ship, plane, imaging) Urgent need to start regular offshore monitoring of birds
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