The methodology and applications of Agricultural

Landscape monitoring in Estonia

Kalev Sepp,

Institute of Environmental Protection

Estonian Agricultural University

EPI EAU

Proposed concept of landscape monitoring program

in Estonia

Landscapemonitoringprograms

Finacialcapacaties,

availabletechnologies

Scientificprinciples

Social andeconomical

values

Experinces ofother countries

Environmentalpolicy

Environmentaland landscape

indicators

Agriculturallandscapemonitoring

Remotesensing of

landscapes

Monitoring ofcoastal

landscapes

Monitoringprotected and

valuablelandscapes

The main purposes of the agricultural monitoring programme were

defined as follows

To follow up and evaluate the environmental effects of land and agricultural reforms

To define changes in land use structure in the different type of agricultural landscapes (intensive and extensive land use)

To study changes in land cover types, especially fallow land and semi-natural areas

To explain the connection between landscape structure indicators and the characteristics of ecological status of agricultural landscapes

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Conceptual scheme of agricultural

landscape monitoring programme MONITORING OFAGRICULTURAL

LANDSCAPES

ENVIRONMENTAL POLICY

LEVEL OF HUMANPRESSURE

- MICROORGANISMS- EARTHWORMS- BUMBLEBEES

SPATIALSTRUCTURE OF

LANDSCAPE

- AREAL ELEMENTS- LINEAR ELEMENTS- POINT ELEMENTS

HISTORICALOVERVIEW OF LAND

USE CHANGES

AERIAL PHOTOS

ENVIRONMENTAL INDICATORS

Monitoring of agricultural landscapes

Spatial structure of landscapeaerial

linear

point

Historical overview of landscape change

Level of human pressureearthworms

epigenic fauna of soils

soil-microorganisms

bumblbees

Test areasRegional distribution according to the Estonian landscape regions

Distribution all over the country

Intensive and extensive areas as well as marginal areas of agriculture

Availability of additional data

Good relation to other sites of environmental monitoring

Localities were divided into two groups• intensively used agricultural areas (IUAA) • extensively used agricultural areas (LIUAA)

Examples from landscape pattern analysis

buffer zones <5m

buffer zones >5m

ecotones between cultivated land and broad-leaved forests

ecotones between cultivated land and mixed or coniferous forests

ditches and brooks

arable land

semi-natural grasslands

fallow land

mixed forests

coniferous forests

broad–leaved forests

bushes

wetlands

cultivated grasslands

pastures

legumes

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Methods of studying human pressure on

agricultural land The following earthworm and soil microbial community parameters were described:· number of individuals and species of earthworms (Lumbricidae) per 1 m2;

·   maximum dominance in earthworm community (%);·  diversity of soil microbial and earthworm communities;·  total hydrolytical activity of soil microorganisms;·   the number of colony forming microorganisms per 1 g of dry soil.

Methods for analysing the landscape diversity

changes in monitoring areas Time period - over the last 50 years Time sections 1950–1955; 1970–1975; 1995–1999Black-and-white interpreted aerial photos in the scale of 1:10 000 were used. Aerial photos were scanned by AGFS scanner (A3) and transformed using the software ARC/Info. Scanned aerial photos were vectorised and encoded in the environment of the software MapInfo and GeoGraphics.

Classification included different polygon elements (e.g. arable lands, forests, grasslands, waters, bushes and brushwood, other lands).

The main objectives of the research

To investigate the relationship between the landscape structure and the abundance and composition of indicator species - bumble bees

To develop a general concept of agricultural landscape monitoring in Estonia

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Methods for bumblebee studies in agricultural landscapes

1. Counts were carried out in nine localities localities in 1996-1999

2. Localities were divided into two groups1. intensively used agricultural areas (IUAA) (the percentage of

arable land was >65%)

2. extensively used agricultural areas (LIUAA) (the percentage of

arable land was <45%). 3. Each locality included two transects (2×1,000 m)

4. Both transects were divided into 20×2 m plots

5. Transects were observed regularly from July to August

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Methods for bumblebee studies in agricultural landscapes

Counts were made by the same observer on the same day in the weather conditions optimal for bees

The following information was recorded each time: bumblebee individuals were encountered species and cast food plant(s) foraged

Study areas (1200 ha each) were mapped on the land use plan in scale 1: 10,000

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Statistical analysis of data sets

Principal Component Analysis (PCA) of the landscape elements and the bumble bee species found was carried out

The results of PCA are interpreted using scatters of the site scores connected with group centroids (star plot), and via correlation circlesThe effect of the grouping factor was estimated using a multivariate randomisation test (Manly, 1995) with 1,000 permutations

The influence of landscape structure on bumble bee communities was studied with co-inertia analysis

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PCA based on the regional bumble bee distribution

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IUAALIUAA

B. veteranus

B. pratorum

IUAALIUAA

The first and second principal components described 30.9% and 18.7% of the overall data variation,

respectively

PCA based on landscape elementsEPI EAU

LIUAA IUAA

PCA on landscape elements matrix described

28.7%, 21.9% and 19.8% of the overall data variation

wetlands

cultivated grasslands

arable lands

Results of co-inertia analysis

The permutation test revealed that the co-structure between the data sets of landscape elements and bumble bee species was highly significant (P<0.001)

The correlation coefficients between the two new sets of co-ordinates for the first and second axes of the co-inertia analysis were 0.87 and 0.97, respectively

The most important elements of landscape and bumblebee

species defining the first two factors of the co-inertia analysis F1 F2LANDSCAPE BUMBLEBEES LANDSCAPE BUMBLEBEES

Ecotonesbetweenagriculturalland andbroad-leavedforests

B. sylvarum Semi-naturalgrasslands

B. pratorum

CultivatedGrassland

B. terrestris Fallow land B. hypnorum

Legumes B.distinguendus

Broad-leavedforests

Wetlands B. schrencki Pastures B. ruderariusMixedforests

B. soroensis

Ecotonesbetweenagriculturalland andmixed andconiferousforests EPI EAU

Conclusion

The correlation circle of the bumble bee species found demonstrated that the species relevant to the grouping of study sites into IUAA and LIUAA were

B. pratorum (7)

B. sylvestris (9)

B. lapidarius (1)

B. veteranus (4)

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ConclusionThe species composition and abundance of bumble bees was to a great degree determined by landscape structure

The permutation test revealed that the co-structure between the data sets of landscape elements and bumble bee species was highly significant (P<0.001)

The correlation coefficients between the two new sets of co-ordinates for the first and second axes of the co-inertia analysis were 0.87 and 0.97, respectively

Bumble-bees can be use as bio-indicators for evaluation natureless of agricultural landscapes

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