Birds as a tool for ecosystem management
onvestigation of ecosystem state is generally a costly and a labor-intensive issue. That is why, at the stage of obtaining data about ecosystem changes, the indicator groups of animals and plants are used. Birds are among accepted indicators of ecosystems, since:
1. They are very sensitive to changes, occurring in landscapes and in cycle of matter in ecosystems.
2. Number of species are easy to recognize visually and acoustically, which makes their surveys easy and allows participation of non-professionals in large-scale counts.
3. Many countries have developed a database on birds during a long time period; it allows analyzing and tracing of population trends for longer period.
Data obtained from monitoring will contribute to the first Atlas: "The State of Breeding Birds of Armenia"
In addition to data, obtained from monitoring, the knowledge about ecology of certain indicator species, and their habitat requirements allows modelling of consequences of economic and political decisions at different levels: starting from individual business-projects and up to introduction of new policies. Such modelling makes possible correction of the projects, policies, etc. depending on predicted consequences; also such modelling helps in development of measures aimed at mitigation of negative effects or at recovery of the lost components of ecosystems.
TEDxYerevan talk about importance of bird monitoring.
Study of bird ecology can be divided into two large categories: long-term studies, which are aimed to reveal trends of changes (number, productivity, etc.), and short-term studies, aimed to reveal connections with other biotic and abiotic components of ecosystem. Our studies cover determination of trends of birds' abundance, distribution and reproductive success, as well as identification of habitat requirements of certain bird species and their communities.
Bird Monitoring Scheme
As a foundation of monitoring methodology we use an approach, proposed by European Bird Census Council in frames of producing of European Breeding Bird Atlas 2.
Monitoring of birds is implemented on line transects or points. Since a 10x10 km square is accepted as a unit of change, the standard European Monitoring Grid 10x10 km is applied to Armenia, and as a result the territory of republic is divided to 374 squares. However, at European scale it is not always possible to cover 10x10 km squares. For that reason a standard of 50x50 km square is applied. Thus, routs and points of count are connected to squares, and each of them has its own identification number (see Armenia Grid Map for getting code for 50x50 km square or 10x10 km square).
Data for bird monitoring may be obtained from two different sources:
1. Unstandardized observations (so called opportunistic data) and 2. standardized counts (data, collected according to standard methodology). Both data may be used to create species distribution maps, and data, collected by second method may be used for estimation of population trends.
1. Unstandardized observations (opportunistic data) are usually provided by birdwatchers and contain minimum data requirements: precise identification of species, observation date, geographic coordinates, name of nearest locality (human settlement, mountain, historical site, etc.), breeding code, name of observer and his contacts. It is desirable to mention whether all observations have been recorded or the list represents only a selection of species. Every comment is useful (time, observation duration, number of people in the group, etc.). Since it's not always possible to record precise geographical coordinates on the spot, information may be provided according to 10x10 km square code.
2. Standardized counts (counts conducted within certain time period), are lead both by specialists and birdwatchers, having proper skills. Counts are implemented during fixed period of time of 1 or 2 hours, when an observer passes the route in a slow motion. It is desirable to make such counts at the time of the day, when birds are most active (as a rule, early in the morning). The best season for bird count is the period between 10th of May and 10th of June, nevertheless, data, collected in March-April and July-August are used as well (for some species, e.g. Lammergeyer or Eagle Owl, the best period of count of breeding pairs is January-February). With this method, there are more requirements to data: precise identification of species, number of observed or acoustically recorded individuals, observation date, geographical location: 10x10 km square code, coordinates of a beginning of the route, start and end times of the count, name of nearest locality (human settlement, mountain, historical site, etc.), breeding code, name and contacts of observer/s. Collected data are entered into standardized protocol (see Bird Count Protocol) and when the field work is over are inputted into database (see Data Input Form).
To calculate population trends we use multi-year data series and process them using TRIM 3.0 software. The index of a trend is called Collated Index, which is calculated using log-linear poison regression; then the deviations are calculated and presented as a linear function, showing populations growth or decline. Statistically significant change is stated on the p<0.05 level, otherwise the population is considered stable.
Study of breeding success is a particular case of bird monitoring. Changes, occurring in ecosystems, e.g. changes in reproportion of different animals or malfunction of chemical cycles, often affect breeding capacity of birds or survival rate of nestlings. Therefore, breeding success is a kind of signal for fast response. Study of such indexes is usually conducted on species with low natural death rate (e.g. large raptors or Ciconiiformes), but, depending on objective (e.g. study of nest predation), research may also be conducted on small Passerines.
Main method of data collection, applied during such studies is two-fold or three-fold visiting of known nests (or finding them in process) and count of occupied nests, eggs, nestlings, fledglings (consideration on collected data depends also on species sensitivity).
For data processing various coefficients are obtained:
1. Nestling number / egg number ratio - coefficient of hatching
2. Fledgling number / nestling number ratio - coefficient of nestling survival
3. Egg number / occupied nest number ratio - coefficient of breeding productivity
4. Fledgling number / occupied nest number ratio - coefficient of breeding success.
Depending on objective one or another coefficient can be selected. Thus, in our research on White Stork (see Projects) we used coefficients of nestling survival rate and breeding success, in research on Peregrine Falcon (see Projects) we used coefficient of breeding success, in research on four species of vultures (see Projects) we used coefficients of breeding success and nestling survival.
The data on bird number may be used for more complex ecological studies, and one of them is a study
of the habitat preferences of a species. For this purpose we compare density/abundance of certain species or their communities with various characteristics of habitat in different parts of the species' range. Then we detect those peculiarities of habitat, which considerably influence the number of birds.
The basis of data collection for this type of research is a point count, associated with data collection on habitat state (e.g. character of relief and vegetation, human impact). Later data on habitat is compared with data on species density. Usually we apply regression models for the analysis (using Generalized Linear Model, sometimes with Poisson error, however the type of model is determined by the character of variables).
In case, when there are too many types of data about the habitat (as it is proposed in research of Semi-collared Flycatcher in forest habitat - see Projects), the regression modelling is preceded by Principal Component Analysis. It allows cutting off variables, which have no considerable impact on species number. Having such information that is geo-referenced, it is possible to move to the next level and use remote sensing maps for decoding and modeling of populations of studied species.