Category Archives: PECBM

Miniworkshop on wild bird indicators 2019

On 11–12th March a miniworkshop dedicated to wild bird indicators took place in Solsona, Spain. The Forest Science and Technology Centre of Catalonia (CTFC) hosted more than 20 experts in bird monitoring thanks kind invitation from Lluís Brotons.

On Monday we discussed the new forest bird indicator. We suggested to produce two types of indicators – General forest bird indicator and Mature forest bird indicator. We also agreed to apply more objective, trait-based approach to species selection which was presented by Simon Butler. We identified future steps to be done to develop the new forest bird indicators and we will present them on PECBMS workshop in Evora.

On Tuesday we discussed possible future development of farmland bird indicator and other indicators such as Montane bird indicator, Mire bird indicator or Urban bird indicator on European and national scale.

On Wednesday EBBA2 meeting organized by Aleksi Lehikonen took place. We discussed potential research projects using EBBA2 data.

Discussions in CTFC.

The workshop took place in a wonderful countryside near Solsona.

Lunch in a local restaurant.

During the breaks there was also some time for birdwatching.

New leaflet “State of common European breeding birds 2018”

In March we have produced new leaflet presenting the trends of 170 common European bird species based on data from 28 countries covering 37 years (1980–2016). The leaflet summarises outputs of this 2018 data update and presents a nice example of use of bird monitoring and atlasing in species conservation. PECBMS and EBBA2 data on the European Turtle-dove helped to identify Prioroty Intervention Areas for this species.

You may download the pdf version of the leaflet.

Of the 170 species covered, in long-term 52 increased moderately, 65 declined moderately and one steeply, while 46 remained stable. In six cases the species´trends remained uncertain.

Of the 170 species, only 168 are included in the common bird indicators for Europe and EU. The Cyprus wheatear (Oenanthe cypriaca) and Cyprus warbler (Sylvia melanothorax) have been excluded as they are endemic species for Cyprus.

All common species (168 sp.) declined by 15 % since 1980, common forest birds (34 sp.) declined by 6 % and the worst decline continued in common farmland birds (39 sp.), of which we have lost 57 % since 1980.

European wild bird indicators. The numbers in italics show the numbers of species in each indicator which are moderately or steeply declining, moderately or strongly increasing, stable and ucertain.

Bird monitoring and atlasing is helpful in the identification of threats and also in steering conservation actions: using the data collected for the Second European Breeding Bird Atlas (EBBA2), experts modelled probability of European Turtle-dove occurrence in Europe on 10×10 km grid. The map was then used to identify Priority Intervention Areas (PIAs) (Herrando et al. 2018), which indicate where conservation interventions might be best directed.

Priority Intervention Areas (PIAs) for European Turtle-dove. Intensity of green colour indicates modelled probability of species occurence.

We thank to

the thousands of skilled volunteer counters responsible for data collation;

Arco van Strien, Adriaan Gmelig Meyling and Tom van der Meij (Statistics Netherlands), Jana Škorpilová and Maaike de Jong who contributed with final data analysis and computation procedure;

Richard D. Gregory, Mark Eaton and Carles Carboneras for their help and valuable comments on the leaflet;

Jiří Bartoš (, Martin Mesnarowski (, Ondřej Prosický ( and Zdeněk Jakl ( for their beautiful photos which they provided for this leaflet;

Anne Teller, Richard D. Gregory, Ruud P. B. Foppen, David G. Noble and Zdeněk Vermouzek for help and general support.

If you wish, ask for the printed version of the leaflet via e-mail: We apologize for a mistake in the printed version of the leaflet. In the legend for the graph of wild bird indicators there are the colours for the common species and farmland species confused. All common birds should be in blue and farmland birds should be in red. We are very sorry for this inconvenience! The pdf for download is corrected.

You may read more on PECBMS website.

PECBMS workshop on EBCC conference 2019

Do you plan to attend our workshop dedicated to the Pan-European Common Bird Monitoring Scheme in Bird Numbers 2019 conference? Here is the programme!

The workshop will take place on Friday 12th of April, 6–7.30 PM in the Grand auditorium (room 102).

Mainly the national coordinators of the bird monitoring schemes contributing to PECBMS will attend, but also other participants are welcome.

Aims: To inform PECBMS coordinators and wider network about progress in our work, to introduce the new tools for computation and data delivery and stimulate discussion about future plans and priorities.

You can download the final agenda of the workshop PECBMS workshop at the EBCC conference.

European bird indicators used in FAO´s report

European common bird indicators produced by the PECBMS were used in the Food and Agriculture Organization of the United Nations (FAO) report: The State of the World’s Biodiversity for Food and Agriculture. The report concludes that biodiversity for food and agriculture is indispensable to food security, sustainable development and the supply of many vital ecosystem services, and further that unsustainably managed production systems are a key threat to bird species.
The EBCC’s wild bird indicators are used in a section looking at threats and trends in bird populations, alongside data from BirdLife International (page 97). You can access the full report, landing page with links to the report (English) and In brief version (EN, FR, ES, AR, ZH, RU) or the digital report.

European wild bird indicators and Trends of common birds in Europe, 2018 update

The Pan-European Common Bird Monitoring Scheme (PECBMS) presents a set of updated European wild bird indicators for the time period 1980-2016.

The indicators are computed for Europe and its regions (West, North, Central & East and South Europe), and EU, New and Old EU states for common farmland, common forest, and all common birds. Both single European and BioGeo regional species habitat classification are used to assess if each bird species belongs to farmland, forest or other indicator.

We also bring updated population trends and indices of 170 common European bird species for the time period 1980-2016 that have been produced by the Pan-European Common Bird Monitoring Scheme (PECBMS) in 2018. The species trends presented are for long time period (from 1980 onwards until 2016) and for last ten years (2007-2016).

What is new in 2018 data update read here.

3. Multispecies indicators 2018

Box Species selection and classification

Supranational species indices are combined in multispecies indicators. These are produced for groups of species according to their main habitat types. To produce precise indicators with small standard errors, it is important to include as many bird species as possible. The rationale behind the construction of composite indicators is that each species is seen as a replicate that may respond in the same way to environmental drivers as the other species and repeats the same signal.

After the supranational species indices have been produced, species are checked for their suitability to be included in the indicators. If a species trend (i.e. multiplicative trend) is classified as ´uncertain´ AND if the index value is > 200 % or < 5 %, data are considered doubtful and the species index and data quality are examined in detail. The decision to exclude such a species from an indicator depends on whether this species was already used in previous versions of the indicators, whether better data can be expected in the near future and whether index fluctuation is believed to be caused either by poor data or by other reasons linked to methodology.

To produce multispecies indicators, we used an indicator tool (MSI-tool) developed in Statistics Netherlands. The tool produces the same outputs as in the previous updates of the indicators and also the smoothed values with confidence intervals and trend of the indicator. For more details on the tool and statistical procedure please check Soldaat et al. (2017).

For some species the available time series started later than first year. In such cases, the multispecies index has been calculated using the chaining method (e.g. Marchant et al., 1990; Ter Braak et al., 1994) that is incorporated in the indicator tool, assuming that the average change in all other species of the indicator reflects the changes of the focal species during the period that is missing.

As with species trends and indices, the interannual consistency of the indicators is examined: new versions are compared with previous ones. In case any inconsistency is found, we investigate whether this is caused by improvements in the data (e.g. improved national data sets, longer time series, new countries contributing their data) or by a computation error.

Indicators are produced for common farmland birds, common forest birds and all common birds. We developed the PECBMS European species classification to classify the bird species.

The indicators are produced for Europe, EU, but also for four European regions (Central & East Europe, North Europe, South Europe, West Europe) and for two EU regions (Old and New EU).

The regional indicators are produced in two versions: one according to the PECBMS European species classification and one according to the regional classification system, which may differ a little.

For list and graphs of the indicators produced, see the latest update of European indicators.

Box Species selection and classification

To produce common bird indicators, species that are to be included have to be selected and classified according to habitat types in Europe.

So far, three versions of our PECBMS European species selection/classification have been produced and used. Initially based on expert judgment and comprising only a limited number of species, the procedure has developed to a more formal classification of species at the level of bio-geographical regions and more than 100 species are nowadays used to produce indicators.

The first set of European indicators was based on 47 common bird species. They were classified by the national coordinators of monitoring schemes and other experts who met at the PECBMS workshop in Prague in 2002 (read the report in the Bird Census News 16/1). The second set of European indicators comprised an enlarged species set, classified according to the publication by Tucker & Evans (1997). Since 2007, when the third set of European indicators was produced, the species classification is based on assessments within bio-geographical regions in Europe, as described below.

The recommendation to classify species at the level of bio-geographical regions in Europe comes from a PECBMS mini workshop held in March 2005 in Lednice, Czech Republic. It would take into account that birds do slightly different things in different places, so that for instance a bird species that is to be considered as a forest species in one region may qualify as a generalist species elsewhere. Also, it would make better use of local expertise.

The procedure initiated in Lednice was approved and developed further at the PECBMS workshop in Prague, Czech Republic, in September 2005 (read the report in the Bird Census News 19/1). Regional coordinators, who were responsible for the production of regional species lists in cooperation with all relevant experts within their regions, were appointed and a time schedule was approved. Distinguished regions were: ´Continental´, ´Atlantic´, ´Mediterranean´, and ´Boreal´.

Note that the bio-geographical regions were used solely for species habitat classification, not for the imputation of missing values when computing supranational population indices, where geographical regions were used instead.


[blue] ´Atlantic´: Belgium, Denmark, France Atlantic (Atlantic part of the country), West Germany Atlantic (Atlantic part of the country), Ireland, Luxembourg, Netherlands, UK.
[yellow] ´Mediterranean´: France Mediterranean (Mediterranean part of the country), Italy Mediterranean (Mediterranean part of the country), Spain, Portugal.
[brown] ´Continental´ (incl. Pannonian region): Austria, Czech Republic, East Germany, West Germany Continental (Continental part of the country), France Continental (Continental part of the country), Hungary, Italy Continental (Continental part of the country), Poland, Switzerland, Romania, Bulgaria, Slovakia, Lithuania.
[green] ´Boreal´: Estonia, Finland, Latvia, Norway, Sweden.

As we are focusing on common birds, abundant and widespread species are to be used preferably. Species with > 50 000 breeding pairs in ´PECBMS Europe´* are considered to be widespread. However, other species could be added too. These are species which are not well covered by generic monitoring schemes, a main source of PECBMS data. Such species are difficult to detect by generic schemes because of their biology, for example those with nocturnal activity (e.g. owls), some congregatory and colonial species or extremely rare species. Data from species specific monitoring schemes would be needed, however this is currently out of the scope and capacity of PECBMS.

Non-native species are excluded, being an unnatural component that doesn´t contribute to the quality of the avifauna.

The species selected were classified to three groups: characteristic farmland species, characteristic forest species, and other species. We classified them according to their predominant regional habitat use – farmland, forest, other: the percentage of the regional population that uses farmland/forest for breeding or feeding (0-25; 25-50; 50-75, >75; situation in 2000). Any links with a driving force were indicated.

Then, we checked if the species selected are sufficiently abundant in the regions, compared species selection and classification between regions, compiled a final species list and circulated it to national coordinators for discussion and approval.

Finally, regional species classifications were combined into the PECBMS European species classification. For this general classification a species was assigned to a particular habitat category if:

  • at least two regions provided their classification and all providing regions agreed, or
  • only one region (minority) classified a species differently than the others.

In some cases a species classification was provided by one region only, but if the species was concentrated in that region and didn´t occur elsewhere in Europe, the species regional classification was accepted as European too.

If regional classifications differed completely, a species was considered as ´other species´.

The final list of species and their classification can be downloaded here.

* ´PECBMS Europe´ is EU 27 + Norway and Switzerland and consists of those countries which already deliver their data to PECBMS or are supposed to do so in the near future: Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Republic of Ireland, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom.

However, some parts of these countries (states) were excluded, mainly because of their far distance to the mainland of Europe: Faroe Islands and Greenland, Svalbard, Azores, Madeira, Canary Islands, Gibraltar.

2. Supranational species indices and trends 2018


2.1. Delivery of national data to PECBMS coordination unit
Box Data checks
Box Participating countries
2.2. Combining national data into supranational outputs
Box Missing values II
2.3. Types of supranational results that PECBMS produces

2.1. Delivery of national data to PECBMS coordination unit

Coordinators of national monitoring schemes deliver their national data to the PECBMS coordination unit annually. For information on data and contributing countries see Box Participating countries.

The data delivered are: the national yearly indices per species, the all-sites yearly totals (= the sum of birds counted across all sites per year) and their standard errors, and the covariances between the yearly figures.
Specifically, national coordinators deliver two files per species – so called out and ocv files (see paper by Pannekoek & Van Strien, 2001 or also see website of Statistics Netherlands) – both produced by TRIM when calculating species indices at the national level. These TRIM output files are accompanied by a species list and by comments indicating any potential problems in the data. These comments are taken into account in further data analysis.
National data are checked for their quality using quantitative criteria (see Box Data checks).

Box Data checks

After receipt, the PECBMS coordination unit checks the national data on species numbers, indices and trends for inconsistencies; it checks for instance if the scheme all-sites totals exceed the national population size known from other sources. Suspicious data coming from countries with more than 10% of PEBCMS European bird populations (see below) are examined in detail. If any problems are encountered, the coordinators of national monitoring schemes are consulted. Additionally, documentation from previous data analysis is checked and used for decisions in some cases. Data with persistent serious data problems are excluded from computation.

Suspicious data – national species data are subject to closer examination when:

  • Slope (Multiplicative) < 0.6
  • Slope (Multiplicative) > 1.5
  • Slope (Multiplicative) standard errors > 0.5
  • Percentage of scheme time totals of the species > 95% of national population size of the species in European Red List of Birds (BirdLife International 2015)
  • Ratio of national population size to scheme time totals > maximum of species population size in European Red List of Birds (BirdLife International 2015)
  • Number of zero counts < 1
  • Number of missing counts < 1
  • Index value < 0.5
  • Index value > 1000
  • Scheme time totals < 1
  • Scheme time totals > 1000000
  • More than one year with index = 100 and SE = 0 present in the results

For these data checks an automatic system has been developed by Statistics Netherlands.

All national indices – species by species – are also checked for their interannual consistency (comparison with previous trends and indices) and all suspicious and inexplicable inconsistencies in indices are examined in detail. Also for this procedure a special tool has been developed.

Box Participating countries

Monitoring data available to PECBMS come from more than 20 countries. The number of countries contributing data and the number of species covered have increased almost every year, as new schemes have started up and provided their data. Almost all EU Member States are now represented, along with some neighbouring countries.

In 2017 data came from 28 countries: Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Republic of Ireland, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom.

Number of countries contributing to the PECBMS has increased since 2003 from 18 to 28. Moreover, some of the countries with a scheme established recently have not contributed their data to PECBMS yet, but are expected to do this in near future. The overall number of species for that the PECBMS produces trends and indices has almost tripled since 2003 (see graph below).

For details and updated information on national monitoring schemes browse country reports.

2.2. Combining national data into supranational

A method has been developed to produce supranational yearly totals and their standard errors across countries by combining the national data. The method takes into account the differences in population sizes per country, as well as the differences in field methods and in the numbers of sites and years covered by the national schemes. Instead of deriving the standard errors in the usual statistical way from count data and model fit, standard errors (and the year-year covariances) that resulted from the calculation of the all-sites totals per country were applied. The results are similar to those that would be produced when the raw data are being used (Van Strien et al, 2001).

To produce supranational indices, the national all-sites totals per species as assessed in the national monitoring schemes are combined. A weighting factor is introduced to adjust for differences in national population sizes, to make sure that a change in a larger national population has an accordingly greater impact on the overall trend than a change in a smaller population. The weighting factor is calculated as the national population size derived from BirdLife International (2015) in 2008-2012 divided by the average of the all-sites totals for 2009-2011. This weighting factor is applied to all other years of the scheme. By this weighting, the yearly scheme totals are converted into yearly national population sizes.

The national European monitoring schemes have started in different years, leading to missing national all-sites totals. An adapted version of TRIM is used to estimate the missing country totals, in a way equivalent to imputing missing counts for particular sites within a country. After these weightings and imputation steps, the national totals are summed up to European totals.

European species indices for a species are computed if data are available from countries which together host at least 50% of the ´PECBMS European´ population of that species. ´PECBMS Europe´ is EU 27 + Norway and Switzerland and consists of those countries which already deliver their data to PECBMS or are supposed to do so in the near future: Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Republic of Ireland, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom.

However, some parts of these countries (states) were excluded, mainly because of their far distance to the mainland of Europe: Faroe Islands and Greenland, Svalbard, Azores, Madeira, Canary Islands, Gibraltar.

Population species trends (multiplicative slopes) are computed and classified in the same way as at the national level.

Box Missing values II

Ideally, in a European breeding bird monitoring scheme all countries start in the same year. If so, it is relatively easy to assess the changes in the yearly all-countries totals of breeding pairs.

The procedure then is as follows. First the national yearly all-sites totals are converted into national yearly total population sizes in that country. To do so, information of total population sizes collected by BirdLife International (2015) is used. A weighting factor is calculated by dividing the population total as assessed by BirdLife International (2015) by the estimated all-sites totals for those years that are covered by population size estimates (BirdLife International 2015) and a monitoring scheme. Subsequently, this weighting is applied to all years of the monitoring scheme, so that the weighted year totals may be considered as the yearly population totals in that country. An example may clarify this. Suppose the estimated all-sites total of species X in the UK amounts 100 in the year 2009 and 110 in 2010. If the population total for the UK would be 1000 in the year 2009, the weighting factor equals 1000/100 = 10. For the year 2010, the population total is 10*110 = 1100.

The standard error of the yearly population totals are (weight factor) x (standard error of all-sites total).

Subsequently, the population totals for each country are summed to yield the supranational population totals for each year. The standard errors of the supranational totals were derived by the following statistical rule: variance of supranational total = variance of country1 total + variance of country2 total + variance of country3 total etc., where variance = standard error2. This rule applies because the estimates of the yearly totals are independent between countries. Finally, the supranational totals are converted into indices.

The national European monitoring schemes, however, started in different years, leading to missing national all-sites totals. Just as was explained in the example for sites above, simple comparisons of the yearly sum of country totals will then give misleading inferences on trends, because lesser countries contribute in earlier years. Again we used an adapted version of TRIM to estimate the missing country totals, in a way equivalent to imputing missing counts for particular sites. Basically we computed indices for four European regions (West Europe, North Europe, Central & East Europe and South Europe) and derived missing national totals from changes in countries in the same European region, as the example shows.


year 1
year 2
year 3
Country 1 population total
Country 2 population total
Country 3 population total
West Europe population total


Thereafter, the regional totals were calculated and summed to obtain European indices as described above.

An extra complication is that also regions differ in the years covered. For instance, the first country in South Europe started its monitoring activities only in 1989, whereas monitoring started in 1982 in Central & East Europe and even earlier in West Europe. To combine totals from the different regions, we performed a third step to combine results using TRIM. All-together we applied a refined hierarchical imputation procedure to combine country population totals.

To produce supranational indices, we used a slightly adapted version of TRIM tailored to combining all-sites totals and their standard errors instead of raw counts per site. Instead of deriving the standard errors in the usual statistical way from count data and model fit, we applied the standard errors (and the year-year covariances) that resulted from the calculation of the all-sites totals per country.

In summary, PECBMS combines data from national monitoring schemes which differ in several aspects. These differences are addressed in order to produce unbiased results with known precision (Van Strien et al., 2001).

Overview of effects of methodological differences in national monitoring schemes:


Difference between countries (national schemes)
Influence on national indices
Consequences for supranational indices
Field method
Include standard error
Number of sites
Include standard error
Site selection method
Remove bias at national level
Index method
Use proper method (TRIM)
Years covered
Missing yearly indices
Estimate missing indices
Population size
Weight indices by national population size


When supranational indices and trends are produced, a control for interannual consistency is carried out just as was done for national data (see details in chapter 4. Quality control). In case some inconsistencies had occurred, they are examined in details to seek whether the inconsistency is caused by enlarged or improved data set or by computation errors.

For details about imputing countries, see computation schedule.

2.3. Types of supranational results that PECBMS produces

Species indices and trends are produced for Europe and its regions (Central & East, North, South, West, and Southeast Europe, East Mediterranean and West Balkan) and for EU and its regions: New (since 2004) and Old EU Members States).

The species indices are always computed for maximum time period given by the country with the longest data set within the region (e.g. indices for West Europe are produced since 1966 given by the starting year of the indices from UK, while indices from South Europe are produced since 1989 given by the starting year of indices from France – see an overview below).

Overview showing list of 28 countries, their start years and region that each country belongs to:


Country/region Region(group of countries) First year Last year
Austria WE 1998 2016
Belgium-Brussels1) WE 1992 2016
Belgium-Wallonia1) WE 1990 2016
Bulgaria SEE 2005 2016
Cyprus2) East Mediterranean 2006 2016
Czech Republic CEE 1982 2016
Denmark WE 1976 2016
Estonia CEE 1983 2016
Finland NE 1975 2016
France3) SE 1989 2016
Germany East4) CEE 1991 2016
Germany West4) WE 1989 2016
Greece SEE 2007 2016
Hungary CEE 1999 2016
Italy SE 2000 2015
Latvia5) CEE 1995 2016
Lithuania CEE 2011 2016
Luxembourg WE 2009 2012
Netherlands WE 1984 2016
Norway6) NE 1996 2016
Poland CEE 2000 2016
Portugal SE 2004 2014
Republic of Ireland WE 1998 2016
Romania SEE 2007 2016
Slovakia CEE 2005 2016
Slovenia West Balkan 2008 2016
Spain SE 1998 2016
Sweden7) NE 1975 2016
Switzerland WE 1999 2016
United Kingdom WE 1966 2016

WE – West Europe
NE – North Europe
SE – South Europe
SEE – Southeast Europe
CEE – Central & East Europe
First year – first year of data time series in a country/region
Last year – last year of data time series in a country/region
Time series for individual species from national schemes are shorter in certain cases.

Countries notes:
1) Data for Belgium were combined from Wallonia and Brussels regions.
2) Data for Cyprus come from two schemes that partly differ in their regional coverage, Volunteer Common Birds Census (2006-2016) and Western Cyprus Common Bird Census (2006-2011). Data from both schemes were combined.
3) Data for France come from two schemes, old (1989-2001) and new one (2001-2016). Data from both schemes were combined but not updated this year.
4) Data for Germany were combined from schemes in former East and West Germany and also newly from old scheme (Häufige Brutvögel alt, 1989-2010) and new scheme (Häufige Brutvögel neu, 2005-2016).
5) Data for Latvia come from three different schemes, two old ones (differ in their regional coverage, and cover the periods 1995-2006 and 2003-2006, respectively) and a new one (2005-2016). Data from all schemes were combined.
6) Data for Norway come from three schemes, Norsk Hekkefugltaksering, HFT (1996-2008), Terrestrisk overvåking, TOV-I (1996-2008) and Terrestrisk overvåking – Ekstensiv, TOV-E (2006-2016). Data from all schemes were combined.
7) Data for Sweden come from two schemes, old (1975-2015) and new one (1998-2016). Data from both schemes were combined.

Although we produce all species indices for the maximum time period (since the first year of time period available), we publish only the European indices since the year 1980 at the earliest since when the index is based on data from several countries – see the latest version of species indices and trends.

The species trends are produced for the maximum time period and for shorter periods as well: since 1980, 1990, 2000, 2007 and 2012. For simplicity only, we publish the long-term trends (the trends which starting year varies from 1980 to 1998) and the ten-year trends (the trends for last ten years, i.e. 2007-2016).

1. National species indices and trends 2018


1.1 Counting birds
Box Field methods
Box Detectability
Box Selection of sample plots
1.2 Production of national indices and trends
Box Missing values I
Box Trend interpretation and classification

Bird monitoring in Europe is organized on a national level; most countries have developed their monitoring schemes rather independently. National monitoring schemes are organized mostly by NGOs with various involvements of other institutions and individuals (governmental agencies, universities, research institutes etc).

The national bird monitoring schemes employ an array of different methods that are approved by PECBMS. The national coordinators provide PECBMS with the results of their schemes according to agreed standards and formats. Each national scheme delivers indices of numbers of individual bird species and trends therein as its main output; the index is the time series of the numbers, the trend is the change in these numbers over the years. These national indices and trends are the source of data for PECBMS, which uses them to compute supranational species indices and trends and multispecies indicators.

For details and contacts to national schemes´ coordinators visit Common bird monitoring schemes in Europe

1.1 Counting birds

Birds are counted using standardised field methods (for details see Box Field methods). Since complete counts are nearly impossible for large spatial units, birds are counted at sample plots selected across a territory of a country (for details see Box Selection of sample plots). Although field methods, selection of sample plots, and also number of years covered differ among European countries, statistical methods can handle this (see chapter 2.2. Combining national data into supranational outputs). Birds are counted within national generic breeding bird monitoring schemes, where all species registered are counted. However, some species are not covered well by these schemes, such as species with nocturnal activity (e.g. owls) or cryptic life style, some clustered and colonial species or extremely rare species. For such species or groups of species, specific surveys need to be set up, but these are not the focus of PECBMS.

Survey results can be affected by the fact that only part of the birds present at a particular site at the moment of counting is detected by an observer. This ´detection probability´ is variable over space and time and may also differ between observers. This should be addressed in field methods and data analysis. New methods are currently being developed to do so.

The majority of field work (i.e. bird counts) is done by volunteers and managed by coordinators. Since bird watching is a widespread activity across Europe and elsewhere, it is often no problem to recruit a high number of volunteers for bird surveys, and this is relatively easy in comparison to other taxa. Just as professionals, volunteers must be able to identify the birds in the field properly, record field data accurately and in proper format and deliver them timely to the coordinators. Since the volunteers do their work for free, one might fear that their work suffers from this, but such fear is unnecessary. Coordinators use a wide array of methods to check the skills of the volunteers and to guarantee a high standard of the data delivered.

One possible problem connected to working with volunteers is the selection of sampling plots. Volunteers might prefer to count in areas that are rich in birds rather than to be directed to plots which have been selected randomly. To solve this problem several national monitoring schemes select sample plots in a stratified random manner. Another problem is that volunteer fieldworkers can leave a scheme at any moment, causing a turnover in the sites counted and missing values. This occurs in any long-term monitoring scheme and statistical techniques and software are widely available and used to solve this problem too.

While the potential risks linked to the involvement of volunteer fieldworkers have been solved, several advantages remain: the running costs of a scheme are relatively low and large-scale schemes are feasible (Greenwood, 2007).

For more details on each national monitoring scheme visit Common bird monitoring schemes in Europe.

Box Field methods

There is no uniformly best field method to count birds. What method is to be selected depends on, among others, the goals of a scheme, the sampling design and the availability of fieldworkers. Three main standard types of methods are available and used by national schemes within PECBMS, sometimes slightly modified for national purposes: territory mapping, line transect and point counts. Point counts along a transect are called point transect counts.

The territory mapping method is probably the most precise, but it is also very time consuming and laborious. It can be used on a limited spatial scale, unless simplified version is used. Nowadays, most national schemes apply either line transect or point counts methods. Each method has its strengths and weaknesses and there is no single rule to choose from them. Standard textbooks (e.g. Bibby et al., 2000; Sutherland et al., 2004; Sutherland, 2006) and also the Best Practice Guide (Voříšek et al., 2008) give detailed overviews of the differences between these two methods.

PECBMS works with national indices rather than with raw data. Therefore, the field method used to produce the national indices is of minor concern as long as this method is standardized through years and provides a reliable, representative picture of a species’ national trend. Learn more in chapter 2, Box Missing values II.

Details on methods and monitoring schemes for each country can be found in Common bird monitoring schemes in Europe.

Box Detectability

For many types of bird survey detectability is an issue because any comparison of the raw ´unadjusted´ counts between sites and through time must assume that the probability of detecting birds is the same. However, some birds present in a study area will always go undetected, regardless of the survey method, how well the survey is carried out, and the competence of the observers. Comparison of unadjusted counts will only be valid if the numbers represent a constant proportion of the actual population present across space and time. Detectability is an important concept in wildlife surveys and has been a matter of much debate (Buckland et al., 2001; Rosenstock et al., 2002; Thompson, 2002) and recent statistical developments.

A solution is to ´adjust´ counts to take account of detectability and a number of different methods have been proposed (Thompson, 2002). The ´double-observer´ approach uses counts from primary and secondary observers, who alternate roles, to model detection probabilities and adjust the counts (Nichols et al., 2000). The ´double-sampling´ approach uses the findings from an intensive census at a sub-sample of sites to correct the unadjusted counts from a larger sample of sites (Bart & Earnst, 2002). The ´removal model´ assesses the detection probabilities of different species during the period of a point count and adjusts the counts accordingly (Farnsworth et al., 2002). ´Distance sampling´ models the decline in the detectability of species with increasing distance from an observer and corrects the counts appropriately (Buckland et al., 2001). The ´binomial mixture´ model uses counts from repeated visits within a period of closed population sizes (Royle & Dorazio, 2008).

Distance sampling is a way of estimating bird densities from line or point count transect data and of assessing the degree to which our ability to detect birds differs in different habitats and at different times (Buckland et al., 2001; Rosenstock et al., 2002). The software to undertake these analyses is freely available at RUWPA website. This method is often recommended because distance sampling in the field, e.g. recording a distance to each bird, or more often recording birds in distance bands (e.g. 0-25 m, 20-50 m, 100 m and over for line transects, 0-30 m and 30 m and over for point transects) is often practical when alternatives are not. While we flag the issue of bird detectability, most breeding bird surveys do not routinely adjust counts when assessing trends. Distance sampling and other methods are useful to provide improved estimates of population sizes, but so far, there is little evidence that detection probability adds significant bias to bird trends (Johnson, 2008).

Box Selection of sample plots

Monitoring schemes contributing their data to PECBMS are based on sampling, i.e. population indices and other results are inferred from a sample of sites distributed across a country. The selection of sampling plots (sites) determines how representative the results are.

The most common methods to select sample plots in generic breeding bird monitoring schemes are free choice, systematic selection, stratified random selection and random selection. Definitions according to Sutherland et al. (2004).

  • Free choice – there are no rules, each fieldworker is allowed to select the plot arbitrarily. This method is prone to bias. Fieldworkers can, for example, prefer to work in areas that are rich in birds. Also, observers can abandon a site that has become less attractive because birds have declined.
  • Systematic selection – plots are uniformly distributed on a grid every kilometer or hundred kilometers (or whatever scale is appropriate). Although this method is considered much better than a free choice, it still might pose a problem for representativeness if the location of plots coincides with a systematic pattern in the landscape.
  • Random selection – sample plots are selected by the generation of randomly distributed coordinates within the study boundary. Random sampling is the ideal method to select sample plots, although with some practical limitations, e.g. some randomly selected plots can be inaccessible. These limitations can be solved by stratified random selection.
  • Stratified random selection – the area of interest is broken down into different sub-areas, known as strata (singular stratum), according to predefined types of habitat, altitude, land use, bird abundance, accessibility of survey sites, administrative or geopolitical boundaries, observer density, etc. Within each stratum, plots are selected at random.

Free choice was the common method in older schemes, but nowadays most of these schemes have been replaced with schemes with some element of randomization. Stratified random selection is the prevalent method of newly established monitoring schemes in Europe.

In 9 countries a scheme with free choice was in place by 2008. In four of them, the old schemes with free choice have been replaced by new schemes using stratified random or systematic choice of sampling sites; these new schemes are combined with data from the old schemes. Improvements in scheme design are ongoing in two other countries. In the Netherlands post-stratification and weighting has been used as the method to reduce potential bias (Van Turnhout et al., 2008). Czech Republic coordinators analyzed the main habitats and their coverage by the monitoring, and discovered that only urban habitats are slightly oversampled; important bias is unlikely. Nevertheless, improvements in the sampling design are planned here too.

There are only three schemes where potential bias needs to be addressed better. They will be focus of further efforts to improve sampling design in the near future.

All in all, thanks to the improvements in plot selection and increased rigour that have been applied, we believe that bias which could affect results at the European level is unlikely.

Information on selection of sample plots in national monitoring schemes can be found in Common bird monitoring schemes in Europe.

For more details on sampling strategy see Best Practice Guide (Voříšek et al., 2008) or standard textbooks on monitoring (e.g. Bibby et al., 2000; Sutherland et al., 2004; Sutherland, 2006).

Population yearly indices and trends are the most important outputs of national monitoring schemes. The index gives bird numbers in percentages relative to a base year, when the index value is set at 100%. Usually, but not necessary, the first year of a time series is chosen as the base year. Trend values express the overall population change over a period of years.

National species indices are produced by the coordinators of the monitoring schemes. They assess yearly all-sites totals per species and compute the individual national species indices in a prescribed way. The count data usually contain missing values, and to impute these they use the predominant statistical technique, that is, Poisson regression, as implemented in the TRIM software (Trends and Indices for Monitoring data, Pannekoek & Van Strien, 2001). TRIM is a widely used freeware program (available via To facilitate the use of TRIM, the software tool BirdSTATs is available too.

Statistically spoken: the basic TRIM model contains both site effects and year effects and estimates missing values from the data of all surveyed sites:

ln μij = αi + γj,

with αi the effect for site i and γj the effect for year j on the natural log of expected counts μij. Missing counts for particular sites are estimated (´imputed´) from changes in all other sites, or in sites with the same characteristics if the basic model is extended with covariates. The assumption is that changes observed in surveyed sites also apply to non-surveyed sites.

The program produces imputed yearly indices and totals for each species. These yearly scheme totals, together with their standard errors and covariances, are collected by the PECBMS coordinator.

For details on TRIM and BirdSTATs, see Box Missing values I.

In case there is more than one monitoring scheme within a country, e.g. an old scheme and a new one (i.e. schemes differ in time span) or different regional schemes (i.e. schemes differ in spatial coverage), the coordinator combines the results per scheme to produce new combined indices per species and per country. A tailor-made software tool called Combine has been developed for this purpose, which also takes into account standard errors of indices of the constituent schemes. The procedure used resembles the one to produce supranational indices from national results (see below).

In addition to national indices, trends are computed to indicate whether long term changes in bird populations are strongly increasing, moderately increasing, stable, uncertain, moderately declining or steep declining (learn more in Box Trend interpretation and classification).

Box Missing values I

Ideally, in a breeding bird monitoring scheme all sites are surveyed every year. If so, it is easy to assess the changes in the yearly all-sites totals of breeding pairs. These totals are usually represented as indices by setting the first year at value 100. But the reality in large-scale monitoring schemes is that many sites are skipped once or several times during the lifetime of a scheme because some fieldworkers enroll years after the start of the scheme, while others drop out after a number of years. Missing counts thus arise and simple comparisons of yearly all-sites totals of breeding pairs give misleading inferences on trends, as a simplified example shows.

The number of breeding pairs of a given species in the example has declined in sites 1 and 2, which were sampled each year. Site 3 was only surveyed in the third year. As a consequence, the yearly total as well as the index would be highest in year 3 if they are based on the simple sum across all sites, which is of course an artifact caused by the enlargement of the monitoring scheme in year 3. Taking the mean numbers of the sites is also incorrect. This is because, in this case, site 3 happens to have more breeding pairs of the species.


year 1
year 2
year 3
site 1
site 2
site 3
missing count
missing count
yearly all-sites total
yearly indices
yearly mean


To solve the problem by simply disregarding site 3 would be a waste of useful information, especially if site 3 continues to be surveyed in the years to come. It is a better solution to estimate (impute) the missing counts with sound statistical methods. Such an imputation makes it possible to compare the years in a fair way, ruling out artifacts and producing more reliable figures.

We use the predominant statistical technique to impute missing values in count data, viz. Poisson regression (log-linear models), as implemented in TRIM software (TRends and Indices for Monitoring data; Pannekoek & Van Strien, 2001). Poisson regression is also available in the generalized linear model modules of many other statistical packages. TRIM is an efficient implementation of Poisson regression to analyze time-series of count data collected in many sites and to produce indices and associated standard errors. It is a widely used freeware program (available via

TRIM implements several log-linear models to impute missing data. The basic model contains both site effects and year effects and estimates missing values from the data of all visited sites. The key assumption is that changes observed in surveyed sites also apply to non-surveyed sites. The next example shows the result.

TRIM produces the following values for the sites in the example:


year 1
year 2
year 3
site 1 count
site 2 count
site 3 count
estimated: 16
estimated: 12
yearly all-sites total
yearly indices


Changes in site 3 have been based on the changes in site 1 and 2. It is clear that the yearly totals and indices now make sense. The same procedure is applied to impute values for sites that had been surveyed in past years, but are not surveyed any more.

Note that such imputation does not affect the trend estimation, just because missing values are calculated from the changes in sites with observations. Estimating missing values only serves a fair comparison between years. Also note that it is not the aim to get reliable information on changes in site 3, but only to get reliable information on trends based on all available information. Imputed values are less valuable than real observations. The major drawback is that the more missing values occur in the data, the wider the confidence intervals of indices will be. This is because imputed values don´t enlarge the sample size; in the example, the sample size for the first two years still is 2.

The basic model may be elaborated by including covariates, such as habitat or region. Any changes between years for non-surveyed sites then are derived from changes in surveyed sites with a similar habitat or within the same region, thereby relaxing the assumption mentioned above. The incorporation of covariates may lead to better model fit, better imputations and smaller confidence limits of the resulting indices and trends. The penalty of not using such elaborate TRIM models is having larger standard errors of indices.

The usual approach to statistical inference for log-linear models is maximum likelihood estimation and associated calculations of standard errors and test statistics. These estimation and testing procedures are based on the assumption of independent Poisson distributions for the counts. Such an assumption is likely to be violated when animals are counted because the variance may be larger than expected for a Poisson distribution (overdispersion), for instance when the animals occur in colonies. Furthermore, counts are often not independently distributed because the counts at a particular point in time may depend on the counts at the previous time-point (serial correlation). TRIM uses procedures for estimation and testing that take into account these two phenomena.

Apart from TRIM, a software tool called BirdSTATs is also available for computation of population indices and trends. BirdSTATs is an open source Microsoft Access database, which is programmed to use and automatically run the program TRIM in batch mode to perform the statistical analysis for series of bird counts in the dataset.

BirdSTATs is capable of importing different kinds of counts data, enables stratification of count sites and selection of subsets of counts data, produces standardised TRIM input and command files and runs TRIM in batch mode for all or a selection of strata, and it collects the output of the batched TRIM runs in a convenient and standardised format to fit the requirements of PECBMS. It can be downloaded on

Box Trend interpretation and classification

In addition to yearly indices, it is relevant to assess the trend over the whole study period. This trend is the slope of the regression line through the logarithm of the indices. This slope, the standard trend estimate used in scientific papers, is called the additive trend in TRIM.

In addition, TRIM calculates the multiplicative trend, which is easier to interpret for laymen. This multiplicative trend reflects the changes in terms of average percentage change per year. If this trend is equal to 1, then there is no change. If the trend is e.g. 1.08, then there is an increase of 8% per year. This means: in year 2, the index value will be 1.08, in year 3 1.08 x 1.08 = 1.17 etc. If the trend is e.g. 0.93, then there is a decrease of 7% per year.

Both trend estimates are different descriptions of the same estimates: the additive parameter is the natural logarithm of the multiplicative parameter.

The multiplicative trend estimate (trend value) in TRIM is converted into one of the following categories to facilitate its interpretation further. The category is not only determined by the value of the multiplicative trend itself, but also by its uncertainty, here its 95% confidence interval (= trend estimate +/- 1.96 times the standard error of the trend).

  • Strong increase – increase significantly more than 5% per year (5% would mean a doubling in abundance within 15 years). Criterion: lower limit of confidence interval > 1.05.
  • Moderate increase – significant increase, but not significantly more than 5% per year. Criterion: 1.00 < lower limit of confidence interval < 1.05.
  • Stable – no significant increase or decline, and most probable trends are less than 5% per year. Criterion: confidence interval encloses 1.00 but lower limit > 0.95 and upper limit < 1.05.
  • Uncertain – no significant increase or decline, and unlikely trends are less than 5% per year. Criterion: confidence interval encloses 1.00 but lower limit < 0.95 or upper limit > 1.05.
  • Moderate decline – significant decline, but not significantly more than 5% per year. Criterion: 0.95 < upper limit of confidence interval < 1.00.
  • Steep decline – decline significantly more than 5% per year (5% would mean a halving in abundance within 15 years). Criterion: upper limit of confidence interval < 0.95.

See the TRIM manual (Pannekoek & Van Strien, 2001) for technical details on the computation.

The Atlas of Wintering and Migratory Birds of Portugal: a new tool for ornithologists

The first Atlas of Wintering and Migratory Birds of Portugal is the largest collective ornithological work of the last 10 years in the country and is finally published. Field work produced almost 4000 hours of census and 150 thousand bird records, covering three quarters of the national territory in systematic visits. In all, more than 400 bird species have been registered. These are extraordinary results for a project of national scope, whose field work was carried out in only two years.

The project was co-funded by the EDP Biodiversity Fund 2010 and involved the following entities: SPEA (Portuguese BirdLife Partner), LabOr – Laboratory of Ornithology, ICAAM, University of Évora, ICNF, Institute of Nature and Forestry Conservation, Institute of Forestry and Nature Conservation, Regional Secretariat for Energy, Environment and Tourism (Azores), and the Portuguese Association of Bird Ringers.

The Atlas is published in Portuguese and is available only online at