Biodiversity Trends and Threats in Europe: Testing a potential marine species trend indicator
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Author: Ed McManus
1 Contents
1. Foreword 4
2. Introduction 5
3. Monitoring Biodiversity and Ecosystem Changes 7
4. Methods: marine component 9
4.1 Geographical scope and classification of the study area 9 4.2 Locating, mobilising and compiling data 13 4.3 Calculation and aggregation 14
5. Results 16
5.1 Evaluation of the available data 16 5.2 A first trial of the indicator 19 5.2.1 Taxonomic groups 19 5.2.2 Regions 21 5.2.3 Pan-European Indices 23 5.2.4 Baselines and long-term data 24
6. Discussion and recommendations 27
6.1 Data mobilisation 27 6.2 Habitats and ecoregionalisation 28 6.3 Composition and aggregation 29 6.4 Species selection and frequency of reporting 30 6.5 Reliability and sensitivity 30 6.6 Relation between the indicator and biodiversity loss 32 6.7 Potential for use at the national and supra-national scales 32 6.8 Towards a European marine biodiversity monitoring framework 32 6.9 Recommendations and next steps 33
Acknowledgements 35
References 36
Annex 1. Potential marine related EU indicators defined at Malahide. 39
Annex 2. The number of species and time series (lines of data) in the pilot indicator per LME. 40
2 Annex 3. Species list 42
Annex 4. Potential for European Biodiversity Monitoring: Marine Data networks 45
Annex 5. Causes of change 47
Annex 6. LME ‘Response’ indicator. 52
Cover shows the Large Marine Ecosystems within European waters. The numbers refer to North Sea (22), Baltic Sea (23), Celtic-Biscay Shelf (24), Mediterranean Sea (26), Black Sea (62) and the Arctic Ocean (64)
Disclaimer The contents of this volume do not necessarily reflect the views or policies of UNEP-WCMC, contributory organizations, or editors. The designations employed and the presentations do not imply the expression of any opinion whatsoever on the part of UNEP-WCMC or contributory organizations, editors or publishers concerning the legal status of any country, territory, city or area or its authority, or concerning the delimitation of its frontiers or boundaries or the designation of its name or allegiances.
Contact information:
Edmund McManus Senior Programme Officer Marine Assessment and Policy UNEP-World Conservation Monitoring Centre 219 Huntingdon Road Cambridge CB3 0DL, UK E-mail: [email protected] Tel: +44 1223 277314 Fax: +44 1223 277136 Web site: http://www.unep-wcmc.org
3 1. Foreword
This report complements the main report of the project, Biodiversity Trends and Threats in Europe, completed by Mireille de Heer (de Heer, M., Kapos, V., and ten Brink, B. J. E., 2005) by providing a marine component to the work. Much of the rationale of the marine component is similar to that of the main project, as is some of the methodology, but there are also substantive differences between the two parts of the project. Therefore, this report is linked to the main report but can also serve as a stand-alone document. It is structured along similar lines to the main project report and includes some of the same introductory material. The major difference between the marine component and the main project are in the analytical method, which is described in detail in this report. The rationale for this and other differences are explained in the relevant sections. Summary
This report is essentially a methodological review and not intended to be a ‘State of the Environment Report’. It presents a trial of a species population trend indicator for evaluating progress towards the 2010 biodiversity target in Europe, using existing data. The indicator integrates trends on different species (groups), and can be aggregated across habitats and countries. Thus, the indicator can deliver both headline messages for high-level decision-making and detailed information for in-depth analysis, using data from different sources, collected with different methods.
Data was mobilised on over 480 historical trends of populations of fish, marine mammals and reptiles, for a total of 109 species. The vast majority of the trends were for fish and birds. These data were aggregated by biogeographical region and then combined to generate a pilot Pan-European scale indicator. From the data collected for this trial, the indicator suggests a decline of commercial fish populations, but an increase in seabird populations between 1970 and 2000.
It was found that the indicator is potentially useful for monitoring progress towards 2010 biodiversity targets. However, the constraints include the limited sensitivity of the historical data, which may lead to inaccurate estimates of species decline; a potential danger of ambiguity because increases in opportunistic species can mask the loss of other species; and bias with regard to the current dataset.
We recommend mobilising additional existing data (particularly for under represented taxa and regions, and for non target species) from national and regional bodies, and elaborating further the criteria for compiling representative sets of species. Based on the approach detailed here, it is recommended that an analysis is conducted culminating in a ‘State of European Marine Biodiversity’ report. For a frequent, reliable update of the indicator, sound, sensitive and harmonised biodiversity monitoring programmes are needed across Pan-Europe.
4
2. Introduction
In response to global concern over the rapid loss of the world’s biodiversity, the 6th Conference of the Parties (CoP) of the Convention on Biological Diversity (CBD) adopted a global target to reduce the rate of biodiversity loss by 2010 (CBD 2002). This target, which was later endorsed by the World Summit on Sustainable Development (United Nations 2002), has also been adopted by a number of regional scale policies and processes. The European Union Sustainable Development Strategy (EC 2001a) and various other European Union policies (EC 1998, 2001 b, c) set similar or even more ambitious biodiversity goals. The Pan-European Ministerial ‘Environment for Europe’ process adopted a resolution on halting the loss of biodiversity by 2010 (UN/ECE 2003).
This widespread adoption of targets for reducing the rate of biodiversity loss has highlighted a need for indicators that will allow policy makers to track progress towards these ambitious goals. Recognising this need, the CoP of the CBD identified a series of biodiversity indicators for immediate testing (UNEP 2004, CBD 2004). Such indicators are needed at national, regional and global levels. In June 2004 the Environment Council of the EU adopted a set of 15 headline indicators for biodiversity to evaluate progress towards the 2010 target (Council of the European Union 2004). This set was recommended by the EU Biodiversity Expert Group and its Ad Hoc Working Group on Indicators, Monitoring and Assessment, and the Malahide stakeholder conference (Anonymous 2004).
Both the CBD decision and the European documents recommend, among other indicators for immediate testing, indicators of trends in abundance and distribution of selected species. Species trend indicators are considered a sensitive measure of biodiversity change (Balmford et al. 2003; Ten Brink et al. 1991; Ten Brink 2000), and one such approach, composite species trend indicators, has been increasingly widely applied. In addition to the global-scale Living Planet Index (Loh 2002) there are several instances of the successful implementation of such indicators, principally at national scales (Jenkins et al. 2004). The UK Headline indicator of wild bird populations (Gregory et al. 2003a) is one example. The European Bird Census Council (EBCC) has used a similar approach to develop the Pan-European Common Bird Index for farmland and forest birds (Gregory et al. 2003b).
The marine environment requires biodiversity indicators as much as the terrestrial one and many similar approaches have been proposed. (For a list of proposed marine related EU biodiversity headline indicators see Annex 1.) However, in the marine realm the players and interests of different parties are different, and in many senses, the boundaries are non-existent. To address the need for regional scale biodiversity indicators for marine ecosystems in (Pan-) Europe, this study set out to identify suitable data and build upon existing methods to develop an appropriate indicator of trends in marine species
5 abundance for use at the Pan-European1 scale. The indicator is intended to inform high- level decision-making on the environment and biodiversity-related sectoral activities by policy makers at the Pan-European and national levels. The indicator should also be suitable for informing the general public on biodiversity trends. In accordance with the set of requirements listed by the CBD (UNEP 2003), such an indicator needs to be, among other characteristics: policy and biodiversity relevant; scientifically sound; broadly accepted; affordable to produce and update; sensitive; representative; flexible and amenable to aggregation.
In this paper, we present a trial of an indicator to evaluate progress towards the 2010 target for marine biodiversity in Europe, an evaluation of the existing data available for the purpose and our experience of mobilising them, and recommendations as to how the data and the method can be improved based upon this pilot experience.
1 Albania, Austria, Belgium, Bosnia-Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Liechtenstein, Lithuania, Luxembourg, Macedonia FYR, Malta, Netherlands, Norway, Poland, Portugal, Romania, Serbia and Montenegro, Slovakia, Slovenia, Spain, Sweden, Switzerland in the process of becoming a member country, Turkey and the United Kingdom. The Pan-European country list from the European Environment Agency website (http://www.eionet.eu.int/countries.html) has been adopted for the purposes of this project.
6 3. Monitoring Biodiversity and Ecosystem Changes2
A real reduction in species richness is relatively rare in marine systems where gross changes to the environmental medium are infrequent. Most changes in biodiversity are concerned with relative frequency, however, the fact remains that fishing in particular can reduce numbers of certain species to what might be termed “functional extinction” which is reflected in a functional simplification of food webs and trophic linkages within the ecosystem. Consequently, some assessment of functional extinction is possible which can be incorporated into a view of whole ecosystems. Nevertheless, the principal effects on biodiversity tend to be expressed through changes in relative frequency in response to gross changes caused by extraction of certain target species together with the more subtle interplay of impacts of chronic pollution and nutrient run-off within possible long-term natural shifts. To measure total biodiversity changes requires indicators of the integrated impact of all of these processes and there may be a limit to the extent a single species can do this irrespective of the extent of its connectedness.
A number of approaches have been developed based upon use of wider criteria within marine ecosystems. Caddy (2004) has described an approach using multispecies indicators in the Black Sea where an integrated assessment is achieved through an evaluation based on a number of indicator species from different trophic levels and their trends, in comparison to a specified baseline period (before the 1989 Mnemiopsis upsurge, in this case). From these overall ‘scores’ a picture emerged that after the Mnemiopsis explosion resident pelagic and some other smaller species where recovering but that many benthic and immigrant (i.e. migrating from the Mediterranean) groups were continuing to decline. Whilst this approach, as with the others outlined below, has been developed more with a view to fisheries management, nevertheless the implications for biodiversity assessment are clear.
Other integrated indicators have used gross shifts in trophic structure as evaluators of change. The process of “fishing down the foodweb”, whereby the typical fisheries process of taking the apical predators and larger species first, leads to a general reduction in the “mean trophic level” or in changes in the predator species/prey species catch/biomass ratio (Pauly et al. 2001). The mean trophic level or pelagic/demersal index will also decline if instead of, or as well as, removal of top predators, the primary production base of the pyramid is inflated by nutrient enrichment from increased run-off or organic pollution (de Leiva Moreno et al. 2000). These indicators can give a view on top-down or bottom- up processes. In the case of the Black Sea (Caddy 2004; Fig 11.1) the pelagic/benthic ratio declines, most probably as the top predators are removed and then begins to rise again as the resident pelagics recover but also as the benthic species, such as turbot, fail to recover most probably due to the degradation of the benthic environment as indicated by the Phyllophora demise (Caddy 2004).
2 Based on Payne and McManus 2004.
7 However, focusing on a single species or trophic level change indicator from the outset may exclude important (representative) datasets. It is suggested here that a more inclusive approach is required, in an often data poor environment, to incorporate trend data on multiple species.
8
4. Methods: marine component
The marine environment presents a number challenges for developing an indicator on the trends in abundance of species. These include defining the geographical scope and classification for the indicator, finding appropriate data, selecting the component trends for inclusion and combining them in a way that is representative of the system and trends of interest.
In the first instance we must ask ourselves why we need to divide the marine environment at all. The answer is that regionalisation is necessary for assessment and management purposes. For example, information on the population status of a particular species of zooplankton may present a very different message about the state of the North Sea as opposed to the state of the Arctic Ocean. It provides a context for information to be understood, and to a certain extent facilitates the identification of what information is required to assess a particular area of the Seas or Oceans. Also, in a very practical sense, it enables the effective management of the marine environment and avoids a ‘one size fits all’ approach to the mitigation of pressures.
However classification of the marine environment lags behind that of the terrestrial environment. Efforts to devise global systems have largely been concentrated on biogeography and have been confined to the latter half of the present century: none have been globally accepted. One specific problem that sets marine classification systems apart from those of a terrestrial nature is the three-dimensional nature of the habitat, and the differentiation between benthic and pelagic systems. Most ecosystem or habitat classification schemes have focused on benthic systems, and the definition and differentiation of pelagic habitats remains unclear. By contrast the differentiation of global biogeographic patterns has focused much more closely on pelagic patterns.
4.1 Geographical scope and classification of the study area
There already exists in European seas a number of ways of dividing up the waters in a supra-national fashion.
The oldest divisions of European seas are the boxes defined by the International Council for the Exploration of the Seas (ICES) from the early part of the 20th century, principally in connection with the collection of data for fisheries management. More recently this was extended to the concept of Eco-regions determined in response to a request from the EU to assist in the implementation of the ecosystem approach to management (ICES 2004).
The EU itself has already adopted the Regional Seas approach to dividing up maritime systems as has the United Nations through UNEP. A management and advisory body has been set up by the EU for each sea. They are currently carrying out their own monitoring
9 and assessment programmes. Also, an agreed, comprehensive system of habitat classification, such as is under development in EUNIS (EUropean Nature Information System), is important for characterizing marine (including pelagic) and coastal habitats. Mapping of coastal, offshore, and deep-sea habitats is necessary for the protection of sensitive, essential (e.g., spawning or nursery grounds), or rare habitats as well as for a better understanding of their associated ecosystems. While work is ongoing in some countries and areas, there is still much to be done and habitat surveys, especially in deep- sea areas, are very resource demanding.
The United Nations has also divided up the oceans through FAO more specifically for collection of fisheries data. In Europe, FAO recognizes the ICES divisions with which its own division is consistent. In the Mediterranean, however, where there are not ICES boxes, the FAO has derived its own divisions.
The 1992 OSPAR Convention is the current instrument guiding international cooperation on the protection of the marine environment of the North-East Atlantic. It combined and up-dated the 1972 Oslo Convention on dumping waste at sea and the 1974 Paris Convention on land-based sources of marine pollution. The work under the convention is managed by the OSPAR Commission, made up of representatives of the Governments of 15 Contracting Parties and the European Commission, representing the European Community, and is organized under six strategies including protection of the marine environment, and monitoring and assessment.
There have, however, been reservations expressed by both EAM and EMMA (advisory groups within the EC on ecosystem based approach to management and marine monitoring and assessment) that existing divisions may not be so consistent with the need for ecosystem-based divisions and that divisions based on straight-lines around a box may be more administrative rather than functional in their criteria. A rather more radical approach in this respect has been the development of the concept of Large Marine Ecosystems (LMEs) over the last two decades (Sherman et al. 1990) where ecosystem parameters are central to the divisions.
Large Marine Ecosystems Large Marine Ecosystems are regions of ocean space encompassing coastal areas from river basins and estuaries to the seaward boundaries of continental shelves and the outer margins of the major current systems. They are relatively large regions on the order of 200,000 km2 or greater, characterized by distinct: (1) bathymetry, (2) hydrography, (3) productivity, and (4) trophically dependent populations. Several LMEs occupy semi- enclosed areas, such as the Black Sea or the Mediterranean. They can be divided into sub-areas (e.g., the Adriatic Sea). Others are limited by open continental margins (e.g. the Northwestern Australian shelf). Their seaward limit extends usually beyond the continental shelf to around the 200m-depth contour. On a global scale, the 64 LMEs produce, 95% of the world’s annual fishery biomass yield. Within their boundaries most of the global ocean pollution, overexploitation of fish and fisheries, and coastal habitat alteration occurs (Sherman 2003).
10 The LMEs thus provide an ecologically based way of dividing up European waters into units that can, and have been, used as units of assessment and policy formulation (e.g. Baltic, Black Sea). The Pan-European marine ecosystems covered in this study are divided into 13 LMEs (Figure 1). Because they are already used as a basis for monitoring and assessment, they come with a ready-made five-module array of indicators, based on production, fisheries, pollution/ecosystem health, socio-economics and governance that support policy development. Using these units as the basis for a species trend indicator will add to the strength and utility of the existing indicator base, which in turn provides context for biodiversity-related decisions.
The strengths of the LME approach have recently been reviewed in a status report on the LME Program (NOAA 2004). In summary these include the following points:
• LMEs correspond to natural features and are delineated on the basis of four ecological criteria - bathymetry, hydrography, productivity, and trophodynamics. • There has been a high degree of international acceptance of the LME approach particularly within the USA and via the GEF International Waters Programme. • LMEs can accommodate scaling at different levels and various kinds of management jurisdiction. • Using science-based population assessments, calibrated against identified and accepted suites of ecosystem indicators of changing states of LMEs (productivity, fish and fisheries, pollution and ecosystem health, socio-economic, and governance), recovery of depleted fish stocks has been initiated in some parts of the world. • Information pertinent to the assessment and management of LMEs is readily available on several websites. Time series information and trends on resources at the LME scale can be found on FAO, University of British Columbia, and NOAA websites; descriptions of the state of all 64 LMEs can be found on the NOAA website. • Modelling of nitrogen over-enrichment in LMEs, and modelling of carrying capacity for all 64 LMEs, is underway.
However some of the challenges with this approach include:
• The exclusion of the open ocean areas (i.e. large areas of the North East Atlantic). The valid rational for this is that 95% of all extractive fisheries operations and most pollution happens in the shelf areas although this is rather a production, human-use orientated approach. In terms of resource management these are clearly priority areas. Whether the lack of immediate inclusion of the open oceans in the LMEs is important depends upon the policy objectives of the institution in question.
11 On balance the LME approach is appealing for use in this methodological review as it is ecologically based and offers an internationally accepted unit for use in monitoring the marine environment (with a growing consensus in Europe).
Figure 1. Large Marine Ecosystems (LMEs) as defined by NOAA, which fall within Pan- Europe: (KEY: 19- East Greenland Shelf, 20 - Barents Sea, 21 - Norwegian Shelf, 22 – North Sea, 23 - Baltic Sea, 24 - Celtic-Biscay Shelf, 25 - Iberian Coastal, 26 - Mediterranean Sea, 27 - Canary Current, 59 - Iceland Shelf, 60 - Faroe Plateau, 62 - Black Sea and 64 – Arctic Ocean)
12 4.2 Locating, mobilising and compiling data
As in the terrestrial environment, not all types of organisms are suitable for inclusion in a marine species trend indicator. This is due both to issues of data availability and to factors such as natural patterns of fluctuation. Phytoplankton composition, for example, changes extensively with the seasons and not always in a predictable fashion. The short generation time of days to weeks means that they can respond quickly to relatively small shifts in environmental conditions and therefore are rather too labile to represent longer term changes. Similarly, the relatively short generation time of smaller zooplankton means they may not be useful for inclusion. At the other end of the scale, some of the large migratory open water species of fish, such as tuna or swordfish, reptiles or mammals, have such a capacity of determining their own distribution and for crossing between major ecosystems as to have a limited use. Alien invasive species are also inappropriate for inclusion in species trend indicators, as rises in their populations are usually counter-indications of ecosystem integrity. Within these constraints, the basic strategy of this study was to compile as many data on population trends for as many species as possible (within the 13 Pan-European LMEs).
The main focus for data collection was on populations of fish, marine mammals and seabirds. However data for other taxa were also sought, including: crustaceans; sharks, skates and rays; molluscs; turtles (in enclosed basins), jellyfish and marine worms.
There is a wide variety of marine assessment and species monitoring activity in the Pan- European study region. The most developed of these relate to stock estimates of commercially important fish, and also to birds. Other sources of data on population trends include academic studies and site monitoring programmes.
To access relevant data arising from these and other efforts, we held initial consultations with the Joint Nature Conservation Committee, Centre for Environment, Fisheries and Aquaculture Science, Plymouth Marine Laboratory, Marine Biological Association and English Nature to understand ongoing work within the UK and to identify other key actors managing marine data in Europe. Data mobilization was attempted by assessing the relevance of data from global processes and conventions (specifically the Convention on Migratory Species and Convention on International Trade in Endangered Species of Wild Flora and Fauna), and from regional and national organizations and networks (MarBEF3, EDIOS4 etc.). Data were also sought from species orientated networks e.g. The IUCN Shark Specialist Group and the IUCN Marine Turtle Specialist Group.
Table 1 summarises the main data sources used in this project.
3 Marine Biodiversity and Ecosystem Functioning http://www.marbef.org/ 4 European Directory of the Ocean-observing System.
13 Table 1. The marine taxonomic groups included in the study and the key custodians. Species Organisation Website group Sea birds BirdLife International http://www.birdlife.net/ Fish International Council for the Exploration of the Sea http://www.ices.dk/ (ICES) Marine ICES http://www.ices.dk/ mammals North Atlantic Marine Mammal Commission http://www.nammco.no (NAMMCO) Reptiles IUCN Marine Turtle Specialist Group (Green turtle http://www.iucn-mtsg.org assessment)
Please see the discussion (section 6) for an elaboration on data mobilisation and agencies that were contacted.
4.3 Calculation and aggregation
The original data were collected by a wide variety of methods, including: - Standardised monitoring schemes with fixed sampling sites. - Modelled estimates of parts of the population (spawning stock biomass). - Estimates of total population size, either by direct observation or indirectly, e.g. Catch Per Unit Effort (fish), sightings per unit effort (whales). Therefore, the original data were expressed in different units, and an approach was needed that would allow combination of these different data types.
For the marine component we chose to adopt the Living Planet Index (LPI) approach to species trend index calculation (Jenkins et al. 2004). The reasons for this choice were as follows:
1) The units of the population data varied between and among species. LPI can use multiple types of population units within any one calculation of the index. 2) The output is extremely easy to comprehend. Interpretation of upwards and downwards trends are easily distinguished. 3) It accommodates weighting for different groupings of data (e.g. by region / taxonomic group). 4) It accommodates patchy data.
In essence the LPI is able to utilize the scarce and varied data on marine species population trends that does exist, and it can efficiently and effectively communicate the results in a meaningful way.
The LPI was first developed in 1997 by WWF (Worldwide Fund for Nature) and WCMC (the World Conservation Monitoring Centre) and published in 1998 in the Living Planet Report (Loh et al. 1998) as a contribution to the WWF Living Planet Campaign. It was originally conceived as an attempt to answer the question, “how fast is nature disappearing?”
14
The LPI is an index based on an underlying dataset of population trends in a large number of animal species from all around the world. In effect, the trend line represents the average change within the entire collection of population samples within the study period, giving equal weight to each species, whether common or rare, and to small and large populations. To generate the index, the geometric mean change in all populations is calculated by averaging the logarithm of all data points for each five-year interval and then finding the anti-logarithm. This approach avoids unequal weighting due to population size and the asymmetry associated with using percent change (i.e. a change from 100 to 5 is a 95% decrease, but change from 5 to 100 is a 2000% increase). This approach is analogous to that used in the calculation of the terrestrial species trend index in the Biodiversity Trends and Threats in Europe project.
An arbitrary baseline at the start of the period analysed is then set (in the case of the LPI the baseline is set at 100 for year 1970) and the population change calculated for each successive five-year interval. For presentation, a trend-line is drawn between the geometric mean population values for each period (despite the fact that the composition of the population sample is not entirely constant across periods). The resulting graph illustrates trends in the population samples, and may be a powerful means of communicating information about trends in ecosystem condition, if it is assumed that this sample is representative of trends in a significant proportion of the species in some given area or habitat. The global marine species population index that forms part of the global LPI in 2004 included 217 bird, mammal, reptile and fish species found in marine and coastal ecosystems.
Full-scale species population trend indices have already been applied to monitor changes in biodiversity and progress towards biodiversity targets at national level. Both the UK and the Netherlands have embraced these approaches for generating national level indicators. Applying the LPI approach in unmodified form would in theory produce a general indicator of the trends in biodiversity within a country (or a region) that could be the basis for assessing progress in relation to general biodiversity goals like the 2010 target. It is also possible to use the approach to examine trends in subsets of species that relate to particular policy questions. For example, sub-setting marine species could allow one to address issues related to coastal management or fisheries policies.
Sensitivity analyses for the global LPI suggest that in this case – that is a very heterogeneous data set intended to provide a wide-scale picture – a minimum of around 45 populations is needed to produce an index with an acceptable associated variance. Smaller samples may still be useful if these represent a high proportion of the total number of species in the set being sampled (e.g. the UK farmland birds index is based on only 19 species, but these are a high proportion of the total number of farmland bird species in the UK), or if all the species in the sample show similar trends and therefore the overall trend has low intrinsic variance (Jenkins et al 2004).
15 5. Results
5.1 Evaluation of the available data
Although there are a wide range of assessment and monitoring programmes in the marine areas covered by this study, very few population time series data were available that are appropriate to the calculation of a species trend indicator. Many of the available datasets were from ‘one-off’ censuses (particularly in the case of marine mammals). Other data sets involved counts or estimates repeated for the same species, but in different areas at different times. In other cases data from different censuses could not be combined to produce trends because they employed different methods and/or use different population units (e.g. marine turtles).
In total, for the European marine ecosystems, there were 480 appropriate time series available for 109 unique species, which are mostly birds, with substantial numbers of ray- finned fish and marine mammals (Table 2).
Table 2. The total number of species and total number of time series obtained for Pan- European marine ecosystems CLASS SPECIES NUMBER OF NUMBER OF TIME GROUP SPECIES SERIES COMMON NAME Aves Birds 68 324 Actinopterygii Ray finned fish 27 86 Mammalia Marine mammals 12 68 Elasmobranchii Sharks and rays 1 1 Reptilia Marine reptiles 1 1 Total 109 480
The number of species per year used in the analysis is given in Figure 2. As mentioned in section 4.3, the minimum number of species to be included in the analysis to ensure an acceptable level of variance is 45. Therefore the portion of the data that is statistically robust enough for interpretation is from 1971 to 2000 were there was a minimum of 64 species (1971 value). The number of species per LME ranged from 4 in the Canary Current and Iberian Coastal LMEs to 56 in the North Sea (Figure 3). For a complete breakdown of the number of time series and species by LME see Annex 2. There were data for some species in more than one LME and for some species and LMEs there were multiple time series within a single LME. For a complete list of all of the species used in this analysis and trial indicator development please see Annex 3.
16 Number of species used in the analysis per year .
120
100
80 Fish Birds 60 Mammals
Number Reptiles 40 All taxa
20
0 1931 1937 1943 1949 1955 1961 1967 1973 1979 1985 1991 1997 2003 Year
Figure 2. The total number of species used in the analysis per year. This figure does not include multiple time series for individual species.
17 Number of species and time series per LME
200 173 180 160 144 140 120 100
Number 80 57 57 60 39 38 30 31 40 2324 11 1316 11 20 4 4 6 7 555 6 7 7 0 Iberian East Coastal Shelf Shelf Sea Black Sea Baltic Sea North Sea Greenland Norwegian Barents Sea Iceland Shelf Celtic-Biscay Arctic Ocean Faroe Plateau Mediterranean LME
Total Species Total Time series
Figure 3: Number of species and time series (lines of data) for each LME.
The numbers of species for which data were available are a small fraction of the total complement of fish and mammal species occurring in each LME (Table 3). As the volume of data available increases, this kind of analysis will be an important means of evaluating the representativeness of the data set.
Table 3. The number of species of fish and marine mammals from each LME included in the pilot indicator in relation to the total number of species occurring. LME Ray Finned Fish Marine Mammals Species Total Species in Species Total Species in in the LME in the LME Indicator Indicator East Greenland Shelf 3 158 0 24 Barents Sea 1 44 0 28 Norwegian Shelf 3 232 1 29 North Sea 12 188 0 25 Baltic Sea 4 157 0 21 Celtic-Biscay Shelf 9 317 1 28 Iberian Coastal 4 585 0 33 Mediterranean Sea 2 599 0 15 Iceland Shelf 8 152 11 24 Faroe Plateau 5 174 0 26
18 LME Ray Finned Fish Marine Mammals Species Total Species in Species Total Species in in the LME in the LME Indicator Indicator Black Sea 4 149 0 5 Arctic Ocean 5 121 0 27
The data were collected for a variety of purposes (Table 4), but within a taxon were collected for a consistent purpose. For example, for fish, the data came from fisheries stock assessments, while the marine reptiles data were collected for threatened species monitoring. This aids in simplifying the discussion on the reasons for a demonstrated change in population.
Table 4. Overview of data quality and units. Species Data Units Reasons For Data Type Data Group Data Collection (Actual / Estimated / Availability Common Index) Name Birds 1) Number of Bird census 1) Actual Readily individuals / 2) Index available breeding pairs 2) Population index. Ray Spawning stock Fisheries Estimated (modelled) Readily finned biomass (tonnes) assessment available fish Marine 1) Number (e.g. pup Threatened / 1) Estimate Hard to obtain mammals production estimates indicator species 2) Actual per breeding colony) 2) Sightings per unit effort (per nautical mile) per species Sharks Spawning stock Fisheries Estimated (modelled) Extremely and rays biomass (tonnes) assessment hard to obtain Marine Number (nesting Threatened Actual Extremely reptiles females) species hard to obtain assessment
5.2 A first trial of the indicator 5.2.1 Taxonomic groups Using the data described above, we calculated species trend indicators using the LPI method (Figure 4) for the major taxonomic groups in Pan-European marine ecosystems as a whole. These showed clearly that sea bird populations in the region have increased markedly since 1970, while fish populations have declined over the same period (Figure 5).
19
Marine Species Population Index (Birds or Fish)
Species 1 Species 2 Species 3
Population Population Population
Figure 4. The most basic Living Planet Index method for combining individual population trends into a species population trend index. All population trends for one species are combined using geometric means and the resulting species are combined in the same way for the overall index.
European Marine Species Populations 1970 - 2003 3
2.5
2
1.5
1
0.5
0 1970 1980 1990 2000 Fish Birds Birds and Fish only Birds and fish equal weight
Figure 5. Species trend indices for Pan-European marine ecosystems, calculated separately for birds and fish. The combined index is then calculated in either of two ways: by using all the available species trends equally (dotted yellow line) or by combining the two taxonomic group indices equally (solid yellow line).
When a simple overall index was calculated using all the 410 available species trend data for these two groups, it appears that populations have risen in European seas since 1970 (dotted line in Figure 5) because of the preponderance of bird species. However, when the indices for the two taxonomic groups are combined with equal weightings, the apparently positive trend is much damped (solid yellow line in Figure 5).
20 These issues of dominance by particular taxonomic groups are particularly marked when one considers the 68 trends for 12 marine mammal species. The vast majority of these data (56 trends) come from the Celtic Biscay Shelf (around Northwest Scotland), where populations have been recovering steadily following their drastic reductions in the last century (Figure 6). Were these combined in any way with the trends for birds and fish shown in figure 3, they would strongly skew an overall index.
European Marine Mammal Species Populations (Scottish and Icelandic data) 1970 - 2003 7 6 5 4 3 2 1 0 1970 1980 1990 2000 Mammals
Figure 6. Species trend index calculated for 68 marine mammal populations in the Celtic Biscay and Icelandic LMEs, showing how strongly positive their population trends have been in relation to a 1970 baseline.
5.2.2 Regions There were viable amounts of data to calculate regional species population trend indices for four individual LMEs and two groupings of adjacent LMEs (Figure 7). These show a broad range of overall patterns. The combined Black and Mediterranean Seas and the northern latitude LMEs both have experienced substantial declines since the 1970 baseline, while the other LMEs show more positive trends. The most extreme of these is the North Sea, where the trend is strongly skewed by the sea bird data. Each of the LMEs (or groupings) that contained a viable amount of data demonstrated a decrease in the fish index and an increase in the bird index similar to that for the region as a whole (Figure 8). Therefore, within and between LMEs the same considerations arise as to how best to combine trends for different taxonomic groups.
21 European Marine Species Populations 1970 - 2003 3.5
3 Northern Latitude LMEs (19, 20, 21 and 64) North Sea 2.5 Baltic Sea
2 Celtic Sea
Mediterranean and Black Seas 1.5 Iceland
1
0.5 1970 1980 1990 2000
Figure 7. Species population trend indices for four individual LMEs and two groupings of adjacent LMEs calculated using the basic (un-weighted) method (Fig. 3) and all available population trends.
North Sea Species Populations 1970 - 2003 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 1970 1980 1990 2000
Fish Birds Fish and birds
Figure 8. The trend indices for birds and for fish in the North Sea, which show a substantial decline of fish species relative to the 1970 baseline and a more radical increase in seabirds. An
22 overall trend indicator for all 56 species is strongly skewed by the large number of bird species trends, while an index based on combing the bird index equally with the fish index may be more representative of the balance between these groups within the ecosystem as a whole.
5.2.3 Pan-European Indices Combining the regional data to produce an overall regional species trend indicator for Pan-European marine ecosystems also requires careful attention to weighting issues. An overall regional index produced using the raw trend data (such as those illustrated in Figure 3) gives a very different picture from one generated by un-weighted combination of individual regional indices (Figure 9) that may have been calculated using very different numbers of component trends (Figure 10). A general index calculated using all the available trend data is strongly skewed by the trends in the most data-rich LMEs, and therefore may not reflect real trends in more data-poor regions. On the other hand, giving trend indices from data-poor sub-regions equal weight to those from data-rich ones runs the risk that the overall index may be skewed by trends in a few species that may not be representative of wider trends within the sub-region.
Marine Species Population Index
LME 1 LME 2 LME 3
Species 1 Species 2 Species 3
Population 1 Population 2 Population 3
Figure 9. Calculating a Pan-European Index through un-weighted combination of indices of individual LMEs as shown here gives a different, and perhaps more balanced picture, than calculating a regional index using the simple method shown in Figure 4.
23 European Marine Species Populations. All LMEs combined. (1970 - 2003) 1.6 1.5 1.4 1.3 1.2 1.1 1 0.9 0.8 1970 1980 1990 2000
All All LMEs equal
Figure 10. Pan-European species population trend indices calculated in two ways. A simple LPI calculation (cf. Figure 4) averages the change in all populations (upper, red line), while a two-stage calculation combines indices from individual LMEs in an unweighted fashion (lower, blue line), greatly damping the influence of data-rich regions.
5.2.4 Baselines and long-term data Figures 11 to 15 use 1930 as the baseline. As stated in section 5.1 the early (pre 1970) portion of the graph is not robust enough at this point in time to allow for meaningful conclusions to be drawn due to the relatively few number of species included in the analysis. However the results are presented here to focus on the importance of the choice of baseline. As can be seen by comparing Figures 12 and 13 with Figures 10 and 7 respectively, the time chosen as a baseline can strongly influence the message on the changing status of the European seas that is derived from the population trend indicator. The population index declines noticeably from a 1930 baseline. It is also clear that weighting all of the LMEs equally can accentuate this trend. Individual LMEs show very different patterns in the change of the index over time (Figure 13).
24 European Marine Species Populations
2 1.8 1.6 1.4 1.2 Fish 1946 - 2004 1 Birds 1930- 2003 0.8 Birds and fish 1930 - 2004 0.6 0.4 0.2 0 1930 1940 1950 1960 1970 1980 1990 2000
Figure 11. Pan-European species population trend indices calculated using a 1930 baseline using the basic method for fish and birds.
European Marine Species Populations (1930 - 2004). 4 3.5 3 2.5 2 1.5 1 0.5 0 1930 1940 1950 1960 1970 1980 1990 2000
All LMEs All LMEs equal weight
Figure 12. Pan-European species population trend indices calculated using a 1930 baseline using either the basic method (upper line) or the method that combines indices for individual LMEs.
25 European Marine Species Populations (1930 - 2000) 4
3.5
3
2.5
2
1.5
1
0.5
0 1930 1940 1950 1960 1970 1980 1990 2000
Northern latitude (LMEs 19, 20, 21 and 64) North Sea Baltic Sea Celtic Biscay Shelf Mediterranean and Black Seas (LMEs 26 and 62) Iceland Shelf
Figure 13. Species population trend indices since 1930 based on all marine species populations for which sufficient data are available.
26
6. Discussion and recommendations
Here we have tested the use of the Living Planet Index as a species trend indicator for monitoring progress towards the 2010 target. In doing so we investigated the existence and availability of relevant data sets. The analysis was conducted at the European and LME levels. The main conclusion was relevant data do exist and can be used for the calculation of a LPI for Europe and its regions. The LPI maybe a very useful approach, although it is important to recognise that data availability is the principal limitation in its application. Conclusions should be drawn in the context of other information (e.g. exploited vs non-exploited status etc of the species, and anthropogenic impacts and response measures/options).
One of the major advantages of the LPI as a marine indicator is its ability to use multiple population data units within one analysis. Depending on the amount of available data the indicator can be aggregated by taxonomic group or functional ecological unit. However care must be taken when aggregating data and choosing a baseline as the results of the analysis may depend on both. As the data may also be aggregated by Large Marine Ecosystem or region, the indicator can be used to deliver a range of different messages to different audiences (headline indicators etc.).
6.1 Data mobilisation
Appropriate data are available from intergovernmental organisations (e.g. ICES) and international species-oriented NGOs (BirdLife). For some taxa, many of these data are easily accessible (e.g. fish and birds), but for other species the data are much harder to acquire, and for many taxa population trend data simply do not exist to any great extent (e.g. sharks and marine reptiles). Data mobilisation was attempted by assessing the relevance of data from global processes and conventions (specifically Convention on Migratory Species and Convention on International Trade in Endangered Species of Wild Flora and Fauna), and from regional and national organisations and networks (Joint Nature Conservation Committee, Centre for Environment, Fisheries and Aquaculture Science, Plymouth Marine Laboratory, MarBEF5, EDIOS6 etc.). Data were also sought from species-orientated organisations such as the IUCN Shark Specialist Group and the IUCN Marine Turtle Specialist Group. Data held by most of these provided very useful information for static assessments of specific localities and / or taxa. However consultations held with these agencies showed that there were rarely any time series data.
It would be advantageous to identify and mobilise further historical trend data for other taxonomic groups, and for those LMEs not included in this (pilot) study. Species groups that have not been covered in this pilot study but for which some data are probably available include other commercial fish, invertebrates, and further data on marine
5 EU Network of Excellence on Marine Biodiversity and Ecosystem Functioning 6 European Directory of the Ocean-observing System. For a brief description of the networks please see Annex 4.
27 mammals and Elasmobranchii (skates and rays). Geographic regions for which further data should be sought include the Arctic, Iberia and the Mediterranean. During the data collection stage it will be critical to incorporate expert opinion on the available data to describe its true meaning and utility, and ultimately to decide whether or not to include any one data set in the analysis.
One approach that should be utilised to provide a greater amount of data would be to incorporate more Catch Per Unit Effort (CPUE) fisheries data. This information has been recorded for many years in Europe but is hard to obtain (specifically the effort data). This is due to the very sensitive nature of the data, both politically and commercially. Further efforts should also be made to obtain smaller scale data sets from sub-national research projects and programmes. Also the LPI database may be further augmented with information on catch rates for non-target species (by-catch). In any case, fisheries data needs to be qualified with an understanding of what the data can tell us, for example CPUE data from 1950 for one gear type may not be analogous to data for the same gear in 2000. This is because of the relative increase in fishing efficiency (particularly the industrial sector). There are year-on-year increases of catch using the same gear in the same area, for the same species in the same season per unit of effort. This is due to advances in fishing technology, e.g. huge increases in CPUE of tunas with the advent of the Puretic power block in Purse seining operations in the late 1950s and massive increases in catches of small pelagics in the Mediterranean with the use of new synthetic fibres in net construction. Using CPUE data to provide population trend information requires adjustments to take account of such factors.
As more data (and more data types) are mobilised then it may become necessary to develop data quality criteria. Such criteria could rank different population trend data by assessment methodology (fisheries dependent and fisheries independent surveys) and regional differences (reflecting capacity to conduct any such assessment). In the case of this trial it was felt that although there was a considerable amount of data available (especially for certain taxa) there were relatively few data types, with only robust estimates included and so a more involved discussion on this area did not seem especially useful at this stage.
6.2 Habitats and ecoregionalisation
LMEs are a useful basis for dividing the seas and oceans into ecologically meaningful units for assessment and management. Species data are available for these regions but in some cases the boundaries of the reporting units (e.g. ICES statistical boxes) do not coincide perfectly with the LME boundaries. Therefore dis-aggregation and re- aggregation may be necessary in the future to make use of the existing data streams.
The LME approach does have some limitations. Within the context of this study these pertain to the exclusion of the open ocean (owing to their focus on the continental shelf). Therefore this approach does not incorporate those areas of the ocean were ecologically and economically important species tend to occur (e.g. deep water fish), and also negates areas were early signs of global warming are likely to occur (e.g. changes in
28 biodiversity). This could be overcome in future assessments by including open ocean areas already monitored by existing mechanisms which currently fall outside of existing LMEs. In European waters the major geographic gap of this approach is in the north east Atlantic where waters north, south and west of the Celtic – Biscay shelf LME are not included. This could be addressed to some extent by including the assessment areas of OSPAR and ICES. However, the major utility of this approach is that it has been a major catalyst for regional co-operation in identifying and working to mitigate pressures in the marine environment. This can be seen by the many formal regional strategic action plans that have been developed as part of the LME process. Also a considerable amount of work has already been carried out in the assessment of LMEs worldwide (summaries of these can be seen at http://www.edc.uri.edu/lme/).
At this point in time there are not enough habitat data to adequately describe the marine habitats within European LMEs, although efforts are underway to provide such data through EUNIS. However when these data do become available a deficiency in the available species data from other sources will further delay our ability to produce indicators of biodiversity trends within individual marine habitat types. See section 6.4 for a suggested approach to compensate for this current gap in our knowledge.
6.3 Composition and aggregation
The degree to which the LPI is representative of overall biodiversity trends is obviously a function of the species composition and the way the data are aggregated. Also dividing the data into different subsets enables different questions to be asked (e.g. what are the trends for threatened marine species in Europe). In this trial application, the lack of any data on groups other than seabirds, marine mammals and commercial fish almost certainly means that some habitats and factors are not reflected very well. As suggested in the terrestrial BTTE report, the weighting procedure proposed and piloted here reduces the risk that individual, data-rich taxonomic groups will dominate the index.
An understanding of the composition of the indicator with respect to the ecological characteristics of the species is also important to ensure that there is an appreciation of how representative the results are of the ecosystem. At present no quantitative criteria are applied to specify the balance among species with different characteristics, e.g. how many sedentary species versus how many migratory species and how many threatened (Red List) species versus how many non-threatened species.
Aggregation of fisheries data should be carried with some degree of caution. Firstly, some data sets are more accurate than others and some time should be spent considering and adjusting for this especially if there is a choice of datasets for the same region/species. Also, in the future, fisheries data (CPUE) should ideally be aggregated according to species, gear type, depth region, season etc. Wholesale aggregation of data without due consideration of these factors may lead to spurious conclusions.
29 6.4 Species selection and frequency of reporting
The availability of data for any given species and the frequency with which it is updated should also be considered when selecting species for inclusion. For example, if the data collection for a particular species occurs only every five years, or is highly sporadic then these species may not be appropriate for inclusion. On the other hand, if commercial fish data are used (including data on vulnerable fish species) then the data could be updated much more frequently (potentially every year). However expert opinion should be used to interpret the conclusions that may be drawn from short-term trend fluctuations (1 to 2 year timescale) as opposed to longer-term trends (5 to 10 year timescale).
To ensure efficient and sustainable monitoring of progress to the 2010 target this approach would greatly benefit from a commitment from data custodians to provide automated feeds to the LPI database for analysis and reporting.
6.5 Reliability and sensitivity
Without adequate habitat data (as identified in section 6.2) or exhaustive species time series from many taxa it may still be possible to evaluate the representativeness of the data according to functional/ecological groups (e.g. pelagic vs demersal). This would help clarify to what extent all the trophic levels and ecological niches are represented, and that the conclusions reached are accurate. For example, it may be possible to use fish alone to adequately describe a LME provided that all of the different trophic levels are represented. One approach to achieving this would be to use maximum total length of each species to classify the species in the LME and then ensure that species representing each size class were included. The following table (Table 5) is a summary of the species and size classes of the fish used in the North Sea LME analysis.
Table 5. Length classes of fish in the North Sea LME analysis. Maximum Length Species Habitat Small fish 25 Sand eel Benthopelagic; 35 Norway pout Benthopelagic; depth range 50 - 300 m 45 Herring Benthopelagic; depth range 0 - 200 m Medium sized fish 60 Mackerel Pelagic; depth range - 200 m 70 Whiting Benthopelagic; depth range 10 - 200 m 70 Sole Demersal; depth range 0 - 150 m Horse 70 mackerel Pelagic; depth range - 600 m 100 Haddock Demersal; depth range 10 - 450 m 100 Plaice Demersal; depth range 0 - 200 m
30 Maximum Length Species Habitat Large fish 130 Pollock Demersal; depth range - 200 m 140 Hake Demersal; depth range 30 - 1000 m 200 Cod Benthopelagic; depth range 1 - 600 m
This demonstrates how species data from a wide range of trophic levels has been incorporated into the analysis for the North Sea. However as all of these are commercial species the representativeness of this dataset for all of the fish fauna maybe still an issue. Maybe it is necessary to include non-commercial species to ensure a robust and meaningful trend of all fish fauna. In a similar way it may be possible to divide the available data into other groupings for further analysis (e.g. threatened vs not threatened, commercial vs non commercial, exported vs non exported etc.).
Further to this point it may be advantageous to highlight what is required at a European level. The European (and CBD) indicators call for trends in 'selected' species, although it is also clear that these species should also be representative of the ecosystem. The advantage of using fish is that much data is already available and fish tend to occupy many of the available trophic levels and niches that exist in any one marine ecosystem. Therefore it is arguable that the type of data used here, although highly focused on fish and birds, may prove a useful way forward in the future. As stated above, if it can be demonstrated that the data for one taxon includes species that are representative of all of the trophic levels then such a data set may be adequate. The important issue is to ensure that future data collection would provide coverage for each trophic level.
One way to define a suitable baseline (year) would be to agree on the biogeographical units to be used. Then use expert opinion to decide which is the most important pressure/s in the vast majority of these units and have a baseline that corresponds to a period of time before this pressure / these pressures were known to have exerted an influence. For example if the agreed unit was to be the LME and expert opinion defined fishing as the key pressure then a baseline starting before the onset of industrial fishing could be used (i.e. around 1950).
Similar to the terrestrial component of the BTTE report a statistical analysis of the reliability and sensitivity of the indicator has yet to be carried out. It should include the calculation of confidence intervals, which would best be done using bootstrapping techniques.
Therefore if the LPI is to be used to monitor progress towards the 2010 target considerable efforts will need to be made to ensure that adequate data (species time series) are employed to deliver meaningful results; the data are representative of the LME (or chosen geographical unit); and the trends are not unduly skewed by one dataset e.g. commercial fish species, threatened species etc (weighting may be employed to enable adjustments); and, that the index is statistically reliable, with confidence intervals assigned.
31 6.6 Relation between the indicator and biodiversity loss
The basic assumption behind this indicator is that, in addition to telling the user something about the trends in the component species, it represents wider trends in biodiversity. These are of interest in the context of policy and decision-making that affect progress towards the 2010 target on biodiversity loss.
In this pilot indicator trial, increases in species populations since 1970 contribute to higher values of the indicator; and decreases to lower values. However, this simplistic approach raises two issues:
1. An increase in a population of a species since 1970 cannot always be considered a biodiversity gain, and a decrease cannot always be considered a loss. This can even be the case for species that are considered characteristic of a certain habitat. Thus, with the approach used, the message of the indicator is potentially ambiguous, which conflicts with the requirement of being meaningful and simple to understand.
2. Biodiversity changes before 1970 (often large losses) are not addressed by the indicator. Changes since 1970 might be very small in comparison to these losses and may differ significantly among countries and habitats. Therefore, change relative to the year 1970 provides incomplete information that will not necessarily be appropriately interpreted by policymakers and the public.
Therefore modeling species abundance under reference (e.g. low human impact) conditions should be used to help resolve ambiguity in the indicator and put recent changes into meaningful context (see section 6.5 on defining baselines). Building such a scenario will require information on historical and geographical trends and qualitative and quantitative ecological knowledge.
6.7 Potential for use at the national and supra-national scales
Currently there may not be enough data at the LME level for the development of robust and meaningful indices for all of the Pan-European LMEs (in the context of this study this was particularly true of the northern latitudinal LMEs), and national waters. With the inclusion of more data this may be possible.
6.8 Towards a European marine biodiversity monitoring framework
There is general agreement that co-ordination at a European level is required to implement long-term marine biodiversity research and rationalise the use of the European research infrastructure. For a summary of some European monitoring networks see Annex 4. Many questions relating to marine biodiversity cannot be adequately addressed
32 at local scales and will require the establishment of a monitoring network of competent agencies across Europe. The development of this network will necessitate the support of a common European biodiversity monitoring framework. This should include guidelines to streamline and rationalise monitoring at a sub-national, national and regional level.
Efforts should be made to develop these monitoring networks in the near future using existing data streams e.g. through ICES or DG Fish etc.
6.9 Recommendations and next steps
The increasingly widespread uptake of the LPI as a communications tool and the adoption of species trend indices at a national level demonstrate that such indices are a resonant and potentially influential tool for capturing changes in biodiversity and communicating these changes to a wide audience. Exploiting them to the full however, particularly at the national or local level, will require increased effort at a number of levels, including: • Increasing the availability of data. • Ensuring that there are mechanisms in place to manage these data and generate the indices. • Ensuring that there is as much stakeholder buy-in as possible and that there are mechanisms in place to disseminate the indices and associated information in a form that resonates with a wide constituency.
Several co-ordinated data gathering exercises should be taken to further increase the quantity of data to improve the representativeness and comprehensiveness of species population trend indices. These include:
1) Improve and extend the searching of academic and scientific literature. It is particularly important that ‘grey’ literature such as national government documents be fully searched and exploited. Improved outreach to amateur and academic networks is another mechanism for identifying unpublished data. Owners and custodians of existing time series data on species populations are encouraged to publicise the existence of the data so that the potential for including them in global monitoring efforts can be assessed, and support can be provided for their use in developing regional and national monitoring programmes. This can most usefully be achieved by informing the CBD secretariat and/or UNEP-WCMC, or by making use of the CBD Clearing House Mechanism to inform the wider community. 2) Collect data on non-target / by catch species. 3) Collect data on other species that have not been incorporated into this study e.g. crustaceans, cephalopods etc. 4) Systematically document expert opinion on data quality (particularly when multiple data sets and units from different regions are to be employed), and conduct a statistical sensitivity and reliability analysis of the indicator. 5) Incorporate site monitoring information and small/local data sets.
33 6) Build a consensus among data custodians to provide harmonised data through live streams or on a regular and frequent basis.
Lastly it is worth restating that policy makers need access to other sources of information than that which relates only to State e.g. Pressure and Response information. Indeed it is critical that this is information is examined to provide a context for a species trend indicator and enable co-ordinated mitigative actions to take place. For the marine environment pressure data are relatively easier to come by than the State data. For example, fishing pressure (landings data) is more easily obtained and reported at a range of spatial scales (local, national and regional). Annex 5 presents landings data by LME, it also demonstrates, using the North Sea as an example, how the data can be broken down by species maximum length. Response information is also more easily obtained. One type of action that is used to protect some aspect of the marine environment is the implementation of marine protected areas (see Annex 6 for a summary of the degree of protection afforded to European LMEs.). Concerted attempts should be made to assess the socio-economic impact of the decrease of the LPI from 1 to 0.5 (e.g. how many jobs would be effected etc).
With these six points in mind and based on the approach detailed here, it is recommended that an analysis is conducted culminating in a ‘State of European Marine Biodiversity’ report. This report should then be used to provide a suitable baseline for monitoring progress towards the 2010 target and include a detailed roadmap for the development of a European Marine Biodiversity Monitoring Framework suitable to support a sustainable (beyond 2010) marine species trend indicator.
34 Acknowledgements
We would like to thank the funder of this study, the UK Department for Environment, Food and Rural Affairs (Defra). We would especially like to thank Andrew Stott (Defra) for his support of the marine component of the BTTE project, and Jeff Kirby (Defra) for comments and assistance during the closing phases of the work.
The following individuals and NGOs are also thanked for their advice on data related issues: Steve Wilkinson, Jane Hawkridge and Jon Davies (JNCC), Paul Eastwood, Simon Jennings, Jim Ellis and Nick Dulvy (CEFAS), Bob Clarke and Paul Somerfield (Plymouth Marine Laboratory), Keith Hiscock (MBA), Hans Lassen (ICES), Rachel Cavanagh (IUCN SSG), and Karen Simpson, Christoph Zöckler and Peter Herkenrath (UNEP-WCMC) for leading on data collection for marine mammals, the Arctic and seabirds respectively.
Jonathan Loh (WWF Living Planet Index) for the analysis and Val Kapos for her editorial support and expert opinion on indicator development.
35 References
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36 EC. 2001c. Environment 2010: Our future, Our choice – the Sixth Environment Action Programme. COM (2001) 31 final – Brussels.
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38 Annex 1. Potential marine related EU indicators defined at Malahide.
The marine related EU headline biodiversity indicators, based on CBD decision and focal areas, declared at the Malahide meeting on ‘Halting the decline of biodiversity -priority objectives and targets for 2010’ (held on 27 May 2004) are presented below.
Status and trends of the components of biological diversity • Trends in extent of selected biomes, ecosystems and habitats • Trends in abundance and distribution of selected species • Change in status of threatened and/or protected species • Trends in genetic diversity of domesticated animals, cultivated plants, and fish species of major socioeconomic importance • Coverage of protected areas
Sustainable use • Area of forest, agricultural, fishery and aquaculture ecosystems under sustainable management
Threats to biodiversity • Nitrogen deposition • Numbers and costs of invasive alien species • Impact of climate change on biodiversity
Ecosystem integrity and ecosystem goods and services • Marine trophic index • Water quality in aquatic ecosystems
39 Annex 2. The number of species and time series (lines of data) in the pilot indicator per LME.
LME BIRDS RAY FINNED MARINE SHARKS AND MARINE TOTAL FISH MAMMALS RAYS REPTILES Species Time Species Time Species Time Species Time Species Time Species Time series series series series series series East Greenland 1 1 37 3 4 4 Shelf Barents Sea 10 298 19 1 11 30 Norwegian Shelf 910 12 311 3 1 1 13 16 North Sea 45 15412 12 19 57 173 Baltic Sea 35 49 4 8 39 57 Celtic-Biscay 21 6313 9 25 1 56 31 144 Shelf Iberian Coastal 214 2 415 5 6 7 Mediterranean 9 3616 2 2 1 1 11 38 Sea Iceland Shelf 4 4 817 9 11 11 23 24 Faroe Plateau 518 5 5 5 Black Sea 119 1 4 5 1 1 5 6 Arctic Ocean 220 2 5 5 7 7
7 Includes 2 stocks of fish (2 time series) shared with Iceland Shelf, Faroe Plateau, and reporting from one species shared with the Iberian Coastal. 8 Includes one line of data shared with the Arctic Ocean 9 Includes 1 stock of fish (1 time series) shared with the Norwegian Shelf 10 Includes 6 species (6 time series) shared with the Barents and North Seas. 11 Includes 1 stock of fish (1 time series) shared with the Barents Sea 12 Includes 8 species (8 lines of data) shared with the Norwegian Shelf. Includes 18 lines of data shared with the Celtic-Biscay Shelf 13 Includes 18 lines of data shared with the North Sea 14 Includes 2 species (2 lines of data) shared with the Mediterranean Sea 15 Includes 1 stock of fish (1 time series) shared with East Greenland Shelf 16 Includes 2 lines of data shared with the Iberian Coastal. Includes 1 species (1 line of data) shared with the Black Sea 17 Includes 2 stock of fish (2 time series) shared with East Greenland Shelf and Faroe Plateau 18 Includes 2 stock of fish (2 time series) shared with East Greenland Shelf and Iceland Shelf 19 Includes 1 species (1 line of data) shared with the Mediterranean Sea 20 Includes 1 species (1 line of data) shared with the Barents Sea
40
41 Annex 3. Species list
Sub Class Genus Species species Common name Actinopterygii Actinopterygii Acipenser naccarii Sturgeon fish Actinopterygii Ammodytes marinus Sand eel Actinopterygii Brosme brosme Tusk Actinopterygii Clupea harengus Herring Actinopterygii Engraulis encrasicolus Anchovy Actinopterygii Gadus morhua Cod Actinopterygii Lepidorhombus whiffiagonis Megrim Actinopterygii Lepidorhombus boscii Megrim Actinopterygii Lophius budegassa Black bellied anglerfish Actinopterygii Lophius piscatorius Anglerfish Actinopterygii Mallotus villosus Capelin Actinopterygii Melanogrammus aeglefinus Haddock Actinopterygii Merlangius merlangus Whiting Actinopterygii Merluccius merluccius Hake Actinopterygii Micromesistius poutassou Blue whiting Actinopterygii Platichthys flesus Flounder Actinopterygii Pleuronectes platessa Plaice Actinopterygii Pollachius virens Pollock Actinopterygii Psetta maxima maeotica Black sea turbot Actinopterygii Reinhardtius hippoglossoides Greenland halibut Actinopterygii Sardina pilchardus European pilchard Actinopterygii Scomber scombrus Mackerel Actinopterygii Sebastes marinus Redfish Actinopterygii Solea vulgaris Sole Actinopterygii Sprattus sprattus Sprat Actinopterygii Trachurus trachurus Horse mackerel Actinopterygii Trisopterus esmarkii Norway pout
Aves Aves Alca torda Razorbill Greenland White-fronted Aves Anser albifrons flavirostris Goose Aves Anser brachyrhynchus Pink-footed goose Aves Anthus petrosus Rock pipit Aves Arenaria interpres Turnstone Aves Asio flammeus Short-eared owl Aves Aythya marila Greater scaup Aves Branta bernicla hrota Light-bellied brent goose Sub Class Genus Species species Common name Aves Branta leucopsis Barnacle goose Aves Calidris alba Sanderling Aves Calidris alpina Dunlin Aves Calidris canutus Knot, red knot Aves Calidris ferruginea Curlew sandpiper Aves Calidris maritima Purple sandpiper Aves Calonectris diomedea Cory's shearwater Aves Cepphus grylle Black guillemot Aves Charadrius alexandrinus Kentish plover Aves Charadrius hiaticula Ringed plover Aves Circus cyaneus Hen harrier Aves Clangula hyemalis Long-tailed duck Aves Cygnus olor Mute swan Aves Falco eleonorae Eleonora's falcon Aves Fratercula arctica Atlantic puffin Aves Fulmarus glacialis Fulmar Aves Gallinago gallinago Common snipe Aves Gelochelidon nilotica Gull-billed tern Aves Haematopus ostralegus Oystercatcher Aves Haliaeetus albicilla White-tailed Eagle Aves Hydrobates pelagicus European storm-petrel Aves Larus argentatus Herring gull Aves Larus audouinii Audouin's gull Aves Larus canus Common gull Aves Larus fuscus Lesser black-backed gull Aves Larus hyperboreus Glaucous gull Aves Larus marinus Great black-backed gull Aves Larus melanocephalus Mediterranean gull Aves Larus ridibundus Black-headed gull Aves Limosa limosa Black-tailed godwit Aves Melanitta fusca Velvet scoter Aves Mergus serrator Red-breasted merganser Aves Morus bassanus Northern gannet Aves Numenius arquata Curlew Aves Pagophila eburnea Ivory gull Aves Phalacrocorax aristotelis European shag Aves Phalacrocorax carbo Great cormorant Aves Phalaropus fulicarius Grey phalarope Aves Philomachus pugnax Ruff Aves Platalea leucorodia Spoonbill Aves Puffinus mauretanicus Balearic shearwater Aves Puffinus puffinus Manx shearwater Aves Puffinus yelkouan Yelkouan shearwater
43 Sub Class Genus Species species Common name Aves Recurvirostra avosetta Pied avocet Aves Rissa tridactyla Kittiwake Aves Somateria mollissima Eider Aves Stercorarius parasiticus Arctic skua Aves Sterna albifrons Little tern Aves Sterna caspia Caspian tern Aves Sterna dougallii Roseate tern Aves Sterna hirundo Common tern Aves Sterna paradisaea Arctic tern Aves Sterna sandvicensis Sandwich tern Aves Sula bassana Northern gannet Aves Tadorna tadorna Common shelduck Aves Tringa totanus Redshank Aves Uria aalge Common guillemot Aves Uria aalge Common murre Aves Uria lomvia Thick-billed murre Aves Vanellus vanellus Lapwing
Mammalia Mammalia Balaenoptera physalus Fin whale Mammalia Balaenoptera borealis Sei whale Mammalia Balaenoptera musculus Blue whale Mammalia Megaptera novaeangliae Humpback whale Mammalia Physeter macrocephalus Sperm whale Mammalia Balaenoptera acutorostrata Minky whale Mammalia Globicephala melaena Long finned pilot whale Mammalia Hyperoodon ampullatus Northern bottlenose whale Mammalia Orcinus orca Killer whale Mammalia Phocoena. phocoena Harbour propoise Mammalia Halichoerus grypus Grey seals
Elasmobranchii Elasmobranchii Squalus acanthias Spiny dogfish
Reptilia Reptilia Chelonia mydas Green turtle
44
Annex 4. Potential for European Biodiversity Monitoring: Marine Data networks
Supported by various donors (e.g. the EC) a number of marine data networks have been and are being implemented in several fields including marine biodiversity. It is suggested that these networks should be utilised to ensure the sustainable nature of any future species trend indicators.
EuroGOOS EuroGOOS is the European association of national agencies for developing operational oceanographic systems and services in European seas, and for promoting European participation in the Global Ocean Observing System (GOOS). EuroGOOS was set up in 1994. At present it has 31 members from 16 countries in Europe, and associate membership from several key European multi-national bodies. EuroGOOS bases its plans on the premise that operational forecasting depends upon obtaining a scientifically planned stream of observational data in real time, or near real time, which are transmitted to modelling centres, assimilated into numerical models, and used to produce simulations and forecasts of the state of the ocean and coastal seas.
EDIOS The European Directory of the Ocean-observing Systems (EDIOS) is one of the initiatives of EuroGOOS. The EDIOS Directory provides an internet-based tool for searching information on observing systems operating repeatedly, regularly and routinely in European waters. It contains metadata on European observing systems such as platforms, repeated ship-borne measurements, buoys, remote imagery, etc. A co-operation between EuroGOOS and the Sea-Search network has been agreed for keeping the EDIOS Directory up-to-date. MarBEF21, A network of excellence funded by the European Union and consisting of 56 European marine institutes, is a platform to integrate and disseminate knowledge and expertise on marine biodiversity, with links to researchers, industry, stakeholders and the general public.
BIOMARE One of the objectives of the BIOMARE project was the selection of a network of Research Sites as the basis for long-term and large-scale marine biodiversity research in Europe (more info: www.biomareweb.org). Among the 100 European Marine Biodiversity Research Sites that provide the geographical skeleton for the implementation of large-scale long-term research in Europe, a small subset of Reference Sites has been
21 http://www.marbef.org/data/sites.php
45 selected. All the information about the BIOMARE sites is put into a fully searchable relational database and has a geographical interface. Deep-sea, ocean pelagic, experimental or extreme habitat sites, proposed during the MARBEF project will be added to this database
46 Annex 5. Causes of change
Results from 29 comparative peer reviewed case studies of forcing driving changes in biomass yields have been published describing 14 LMEs for which excessive fishing effort is the dominant driver, and 13 LMEs where the principal forcing is climate. These include 10 of the LMEs in the geographic scope of this project:
Table 6. The key pressures by LME. LME Name PRIMARY PRESSURES ON LME SECONDARY PRESSURES ON FISHERIES BIOMASS YIELDS LME FISHERIES BIOMASS YIELDS North Sea Fishing Climate Canary Current Climate Fishing Norwegian Shelf Climate Fishing Iceland Shelf Climate Fishing Mediterranean Fishing Eutrophication Baltic Sea Fishing Eutrophication Black Sea Eutrophication Fishing Barents Sea Climate Fishing Iberian Coastal Climate Fishing Faroe Plateau Climate Fishing
The following figures illustrate information on fishing pressure (the data is taken from the Sea Around Us22 and then presented graphically). This data can be aggregated by functional group (e.g. demersal, pelagic, bathypelagic etc.) or by size class (see the North Sea for a breakdown by both approaches)
22 www.seaaroundus.org/
47 Figure 14. Total fish landings (metric tonnes) for the Black Sea
Total landings (MT). All species.
1,000,000 800,000 600,000
400,000 Tonnes 200,000 0
50 55 60 65 70 75 80 85 90 95 00 0 19 19 19 19 19 19 19 19 19 19 2 Year
Figure 15. Total fish landings (metric tonnes) for the Baltic
Total landings (MT). All species.
1,200,000 1,000,000 800,000 600,000
Tonnes 400,000 200,000 0
0 5 0 5 0 5 0 5 0 5 0 7 9 0 95 96 99 195 1 196 1 197 19 198 198 19 1 20 Year
48 Figure 16. Total fish landings (metric tonnes) for the Celtic-Biscay Shelf
Total landings (MT). All species
1,600,000 1,400,000 1,200,000 1,000,000 800,000 Tonnes 600,000 400,000 200,000 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 Year
Figure 17. Total fish landings (metric tonnes) for the North Sea
Total landings (MT). All species.
5000000 4000000 3000000 2000000 Tonnes 1000000 0
5 0 0 5 0 50 5 6 65 70 75 8 8 90 95 0 9 9 9 9 0 19 1 1 19 19 19 1 1 19 19 2 Year
49
Figure 18. Index of landings by size class for the North Sea
Index of landings by maximum total length of fish
15 14 13 12 11 Index of landings 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 Year
Small fish (<30 cm TL) Medium fish (30 - 89 cm TL) Large fish (>89 cm TL)
Figure 19. Index of landings by functional and taxonomic groups for the North Sea
Index of landings
16 14 12 10 8 Index 6 4 2
0 6 4 5 54 58 62 66 70 74 78 82 8 90 9 98 9 9 9 9 9 9 9 1 1 19 1 19 1 19 1 19 1 19 1 19 Year
Demersals Large sharks Small to medium rays
50
Figure 20. Total fish landings (metric tonnes) for the Mediterranean
Total landings (MT). All species.
1,200,000 1,100,000 1,000,000
s 900,000 800,000
Tonne 700,000 600,000 500,000 400,000
0 5 0 5 0 5 0 5 0 5 0 5 5 6 7 7 8 8 9 0 96 99 19 19 19 1 19 19 19 19 1 19 20 Year
Figure 21. Total fish landings (metric tonnes) for the Arctic Ocean
Total landings (M T ). All species.
600,000 500,000 400,000 300,000
Tonnes 200,000 100,000 0
0 0 5 55 65 950 980 990 995 000 1 19 196 19 197 197 1 1985 1 1 2 Year
51
Annex 6. LME ‘Response’ indicator.
The number of protected areas or the total area protected is commonly used as an indicator to describe the level of protection to biodiversity afforded to any one area (or globally). Most of the Parties to the CBD identified protected areas as a key contribution to the protection of biodiversity. Globally there are more than 100,000 sites (Spalding, M. et al. in press) with over 4,000 marine protected areas. About 0.5 % of the world oceans (or 1,893,609 km²) are protected.
The most comprehensive dataset on protected areas worldwide is the World Database on Protected Areas (WDPA23) which is maintained by the UNEP World Conservation Monitoring Centre in partnership with the IUCN World Commission on Protected Areas and the WDPA consortium. Marine protected areas (MPAs) have been designated throughout the European LMEs. MPAs may be implemented with a wide variety of management objectives e.g. conservation of biodiversity, protection of a particular species or habitat etc.
Definition: IUCN has defined an MPA as ‘any area of inter-tidal or sub-tidal terrain, together with its overlying water and associated flora, fauna, historical and cultural features, which has been reserved by law or other effective means to protect part or all of the enclosed environment’.
1990 LME NAME AREA PROTECTED (KM²) LME AREA (KM²) % OF LME PROTECTED Baltic Sea 7,764.88 394,265.07 1.97 Black Sea 1,261.17 461,958.12 0.27 Canary Current 7,291.18 1,125,327.31 0.65 Celtic-Biscay Shelf 2,400.47 759,958.41 0.32 East Greenland Shelf 34,339.69 321,972.93 10.67 Iberian Coastal 596.36 303,958.36 0.20 Iceland Shelf 84.10 316,570.37 0.03 Mediterranean Sea 1,436.66 2,529,210.66 0.06 North Sea 2,732.80 695,625.97 0.39 Norwegian Sea 684.83 1,122,990.42 0.06 TOTAL 58,592.13 8,031,837.62 0.73
23 www.unep-wcmc.org/protected_areas/
52
2004 LME NAME AREA KM PROTECTED LME AREA KM % OF LME PROTECTED Baltic Sea 8,644.35 394,265.07 2.19 Black Sea 1,407.57 461,958.12 0.30 Canary Current 11,255.47 1,125,327.31 1.00 Celtic-Biscay Shelf 2,944.27 759,958.41 0.39 East Greenland Shelf 34,339.69 321,972.93 10.67 Iberian Coastal 603.03 303,958.36 0.20 Iceland Shelf 84.10 316,570.37 0.03 Mediterranean Sea 5,600.66 2,529,210.66 0.22 North Sea 3,045.92 695,625.97 0.44 Norwegian Sea 699.18 1,122,990.42 0.06 TOTAL 68,624.24 8,031,837.62 0.85
As can be seen in the above table (source Spalding, M. et al. in press, 2004, UNEP- WCMC) the percentage of the LME protected varies widely between the LMEs. The lowest value is in the Norwegian Sea with currently only 0.06% protected.
53