LAN DSCAPE AND

URBAN PLANNING

ELSEVIER Landscape and Urban Planning 31 (1995) 153-169

Species diversity of forest islands in agriculturallandscapes of southern Finland, Estonia and Lithuania

Merit Mikk*, Ülo Mander Institute ofGeography, University of Tartu, 46 Vanemuise Str., EE-2400 Tartu, Estonia

Abstract

Plant species diversity ofwoodland patches in the agriculturallandscapes of different geographical regions (Fin­land, Estonia, Lithuania) have been studied. During the field studies (1990-1993) a total of 127 woodland patches was investigated (42 in Finland, 44 in Estonia and 41 in Lithuania). In each wood patch the following variables have been identified: number of tree and shrub species (in Estonia also herb species), area, age, disturbance, distance to the nearest woodland patch, and number of soil types (in Finland, the number of forest site types). On the basis of measured variables the coefficient of biotope heterogeneity, the coefficient of age and disturbance, the gamma-diversity (Whittaker, 1977) and the edge index have been ca1culated. A multiple regression model has been developed and compared with an analogous model designed by Rudis and Ek.

The model describes comparatively weIl the tree, shrub and herb species richness of the woodlots in the Estonian study area. In Lithuania and southern Finland the correlation between variables was significant but relatively low. Therefore, no multiple regression model has been compiled for these study areas. The best correlation has been found with the number of species and the woodland patch area. There was no connection between the species number and isolation level in aIl three areas. The diversity of species increases considerably from north to south. Because of nitrogen loading in small woodlots of the Estonian study area nitrophilous herb species mostly dominate.

Keywords: Isolation; Model; Vegetation; Wood patches

1. Introduction

An agricultural landscape dominates in the glacial plains of aIl three study areas in southern Finland, Estonia and Lithuania (Fig. 1). Wood­lands (0.05-28 ha) are isolated by fields, mead­ows and pastures. Most are remnants of former large forest areas. Several authors have pointed out that isolated woodlands of an agricultural landscape are habitat islands. It has been shown

* Corresponding author: Tel. 372-34-30679; Fax. 372-34-35440; e-mail ylo@geogr.ut.ee.

that the number of species of such forest is­lands-as in real islands-depends on area, de­gree of isolation and time of isolation (Tramer and Suhrweir, 1975; Hoehne, 1981; Levenson, 1981; Scanlan, 1981; Mader, 1984; Werres, 1984; Peterken and Game, 1984; Dzwonko and Loster, 1988). Moreover, the species richness of habitat islands may not always be explained purely in terms of the theory of island biogeography (MacArthur and Wilson, 1967). This is because the number of species is not only a function of immigration and extinction rates but also of habitat diversity and succession which are not

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154 M. Mikk, Ü. Mander 1 Landscapeand Urban Planning 31 (1995) 153-169

t'INLAND

Ht'hinki •

ESTONIA

vation it is important to optimize parameters (size, biotope diversity, isolation) ofisland hab­itats that could serve as nature reserves (Helli­well, 1976; Usher, 1979; Higgs and Usher, 1980; Mader, 1986; Soulé and Simberloff, 1986; Van der Maarel, 1988; Zacharias and Brandes, 1990). Modeling of species richness in island biotopes may be helpful for designing ecologically bal­anced rurallandscapes.

RUSSIA 2. Materials and methods

RUSSIA\

POUND

L1THUANIA

\ ..

• \'ilniuft

LATVIA

i .~.

BVELORUSSIA

• St.d~ artH

Fig. 1. The location of study areas.

considered in the theory of island biogeography (Helliwell, 1973; Simberloff, 1976; Forman et al., 1976; Van der Maarel, 1982; Weaver and Kel­man, 1981; MiddIeton and Merriam, 1983). Likewise, the degree of disturbance (Helliwell, 1973; Levenson, 1981) and edge index (Bark­man, 1989; Mander et al., 1989) are significant factors influencing the species richness of wood­land patches.

Rudis and Ek (1981) have compiled a sys­tems model which is based on the theory of is­land biogeography. This model contains all the above-mentioned factors. The main goal of the present study was to evaluate the Rudis-Ek model and to find ways for its practical applica­tion in J.andscape planning. For nature conser-

2.1. Study areas

AlI three study areas have been chosen on the basis of land-use maps at a scale of diversity 1: 1 0 000. The aim of the choice was to find out the diversity of landscape pattern with different size and shape ofwoodlots (Figs. 2(a)-2(c».

The Finnish study area lies about 5 km from Loimaa town (60 0 53'N, 23°05'E). The area is characterized by glacio-Iacustrine plains with loamy soils and with granite bedrock hills on which Scots pine (Pinus sylvestris) and Norway spruce (Picea abies) forest islands are growing (Mansikkaniemi, 1989). The predominant for­est types ofthis region (southern boreal conifer­ous forest zone) are mesic heath forests where Myrtillus and Vaccinium site types are prevalent (Kalliola, 1973; Atlas of Finland, 1988). The whole loamy plain has been cultivated. How­ever, the woodlots on granite hills are less influ­enced by the side-effects of agricultural activities (i.e. fertilization, pollution with pesticides, etc. ) than Estonian and Lithuanian ones, in spite of having been managed intensively.

The Estonian study area is located on the bor­der of two landscape regions: Otepaa Heights and the Moraine Plain of southeast Estonia (Varep, 1964). The central part of the study area lies at 58° 13'N, 26°28'E. The relief was formed dur­ing the Pleistocene and remodeled by glaciers during the last glaciation. It is characterized by undulating and hilly moraine relief where small hills and kames covered with till mostly domi­nate. The altitude ofthis region is up to 120 m, the relative heights reach up to 20-25 m. The up-

M. Mikk, Ü. Mander / Landscapeand Urban Planning 31 (1995) 153-169 155

lOOOm

N ..

s

'\ \i

(b)

" ~18 " 19 , i't'

"20 •

JOOOm

Fig. 2. Principal schemes of the study areas in: (a) Finland; (b) Estonia; (c) Lithuania.

land soils are mostly podzoluvisols, planosols, and podzols on loamy sands and fine sandy loams with the surface soil organic matter content and pH value in the cultivated fields being 1.6-1.9% and 5.6-6.5, respectively. On the lower parts cul­tivated grasslands on gley soils and organic soils dominate (Soil Map of Estonia, 1988). The zonal types of vegetation are coniferous forests of Ox­alis, Vaccinium, Myrtillus, and Po/ytrichum types combined with raised bogs (Pinus bog type), transition bogs (Pinus-Betu/a bog type), and swamps (Betu/a-A/nus swamp type) (Laasimer, 1965; Lôhmus, 1974). Actually only remnants could be found of the Norway spruce forest as the primary vegetation. Typically, small frag-

ments of the birch or aIder forests of Aegopo­dium, Filipendu/a, and A/nus swamp site types dominate. This area has a strong human influ­ence, most of the land is drained.

The Lithuanian study area is located within the South Lithuanian Upland (45 km to the north of Vilnius) at 54°50'N, 25°20'E. This region is characterized by marginal moraine formations formed by Valdai (Weichselian) glaciation. The soils are chiefly brown leached soils and gray podzolic soils (Atlas of Lithuania, 1981). The zonal types of vegetation are deciduous forests with Quercus rohur and Tilia corda ta (Tilio-Car­pinetum association) or mixed forests (mostly with Pinus sy/vestris and Quercus rohur) but in

156 M. Mikk, Ü. Mander / Landscape and Urban Planning 31 (1995) 153-169

N • ~ s

"36 3., ,. 8137 /~r-, .. ~

40 ~ 39 will

Fig. 2 (continued).

(a)

Betula pendula Roth.

Picea abies L.

Sorbus aucuparia L.

Salixspp.

Juniperus communis L.

Populus tremula L.

Alnus incana L.

Padus aviwn Mill.

Betula verrucosa Ehrh.

Ribes nigrwn L.

Rosaspp.

Frangula a1nus Mill.

Sambucus racemosa L.

o 10 20 30 40 50

%

60

o

70

SOOm

80 90 100

2

2

(c)

Fig. 3. Species occurrence in the study areas: (a) trees and shrubs in Finland; (b) trees and shrubs in Estonia; (c) herbs in Estonia; (d) trees and shrubs in Lithuania.

M. Mikk, Ü. Mander / Landscapeand Urban Planning 31 (1995) 153-169 157

(b)

Betula pendula Roth.

Alnus incana L.

Sorous aucuparia L.

Salix spp. a~"", .... < .. :",;§, ~9 - '~.~ > ~:f.i:.\.,,' -,,:~:;,~'.

34

Picea abies L.

Padus avium Mill.

2

19 Ribes rubrum L.

Ribes a1pinum L. ........... _._, __ ... ____ .. _._. _____ ......... ! 19

Ribes nigrum L.

Quercus robur L.

Pinus sylvestris L.

-fÇ::~~~~=~~18 18

Lonicera xylosteum L.

Rhamnus cathartica L.

Sambucus racemosa L.

Juniperus communis L.

Fraxinus excelsior L.

Betula verrucosa Ehrh.

Viburnum opulus L.

Ulmus glabra Huds.

Ribes uva-crispa L.

Malus domestica Borkh.

Corylus avellana L.

Tilia cordata Mill.

8

o 10 20

.18

18

14

30 40 50 60 70 80 90

Fig. 3 (continued).

most cases re1atively young secondary forests with Alnus incana, Acer platanoides and Betula pendula dominate. Parts of these forests have been planted and no primary vegetation has been found. The whole area is strongly rec1aimed and woodlots are influenced by drainage.

The species occurrence histograms of studied woodlots are shown in Figs. 3 (a) - 3 ( d).

2.2. Data collection

The study began with preliminary investiga­tion of tree and shrub species in the Estonian study area in September 1990. In May 1991 the

list was completed by records of species devel­oping in spring. The list of vascular plant species in 44 wood1ots was compiled. In total, 26 tree and shrub species, and 203 herb species have been found. The herb layer estimation was carried out in May and July 1992. The investigations of the herb layer were made using difIerent plot sizes: 4 m2, 100 m2

, and the whole wood1and area. In a11 woodland patches the average height of trees and shrubs and the percentage cover of the tree and shrub layers were determined. In all wood­land patches the following variables were iden­tified: numbers of tree, shrub and herb species, area, age, disturbance, distance to the nearest

158 M. Mikk, Ü. Mander / Landscapeand Urban Planning 31 (1995) 153-169

(c)

Urtica dioica L.

Fragaria vesca L.

Rubus idaeus L.

Geum rivale L.

Aegopodium podagraria L.

EquisetuJn arvense L. Filipendula ulmaria

Maxim. . Anthriscus sylvestns

Holfm. Dactylis glomerata L.

Lysimachia vulgaris L.

Crepis paludosa Moench

Paris quadrifolia L.

Veronica chamaedrys L.

22

o

Galium boreale L. 19

Equisetum pratense Ehrh. 18

Galium mollugo L. . 18

Geum urbanum L.

Cirsium oleraceum Scop.

Oxalis acetosella L.

Rubus saxatilis L.

Phleum pratense L.

Epilobium montanum L. Maianthemum bifolium F.

W. Schmidt

o 10 20 30 40 50 60 70 80 90

%

Fig. 3 (continued).

wood patch, and number of soil types (Table 1 b ). In the Lithuanian study area the field investi­

gations were carried out in July 1991 and com­pleted in June 1992. Only tree and shrub species were studied.

In all woodland patches the same parameters were studied as in Estonia (Table 1 c). In total, 27 tree and shrub species have been found.

In the Finnish study area, the field investiga­tions were made in July 1991 and in July 1992. Altogether 14 tree and shrub species were found. A biotope heterogeneity in Finnish wood patches has been investigated on the basis of forest site

types because no soi! maps for this region were available. AlI other parameters studied were the same as in the Estonian and Lithuanian study areas (Table la).

2.3. Data analysis

In order to investigate the relationship be­tween the independent variables and the number of species, stepwise multiple and linear regres­sion analyses were used as the forward selection

M. Mikk, Ü. Mander / Landscape and Urban Planning 31 (1995) 153-169 159

(d)

Arer platanoides L.

Alnus incana L. [ "ïl,'.":";

Sorbus aucuparia L. Q'

Betula pendula Roth.

Padus avium Mill.

Populus tremula L.

Quercus rebur L.

Salix spp. --126 Loniœra xylosteurn L. ,..-",. ")'k~'!6i1iii{.."Il-r~"~{~"i<:i\;-::':123

Fraxinus excelsior L. -, li~~~'f:'2'7i~;,';!;·:·i:';<·.':·<f22

Picea abies L. ';~'i,;~,\'$:J" .' .. ' ." ',JiÎIT.>'i.11.~;'~";,b!~'i·q20

Pinus sylvestris L. .'W~i,\:}i~~!}'tJij;,tiJllj'~~'ij'~]':;J,.;~;i.~'l.~'.ï'·.F·118

Ribes rubrum L. ::JYt~'i1fo,'{~~~_7$,{~~t\1t1~!!2111

Tilia cordata Mill.

Ulmus glabra Huds.

Frangula a1nus Mill.

Carpinus betulus L.

Euonymus eropea L.

Malus domestica BoridJ.

Ribes a1pinurn L.

Rosa canina L.

Corylus avellana L.

Ribes nigrurn L.

Vibumurn opulus L.

Betula verrucosa Ehrh.

Cerasus wlgaris Mill.

Pyms communis L.

o 10 20 30 40 50 60 70 80 90

%

Fig. 3 (continued).

procedures were used (Jongman et al., 1987). In multiple regression standardized partial coeffi­cients were used. The higher absolute value of the coefficient, the more influence the independent variable has on the number of species.

The species-area relationship was investi­gated using multiplicative regression. The signif­icance of differences was tested using the analy­sis of covariance (F-test). Differences between independent and dependent variables between the woodlands were evaluated with t-tests.

AIl statistical analyses were made using the MS

DOS statistical graphies system Statgraphics 5.0. For the data visualization the Macintosh com­puter graphie programs CA-CricketGraph 111/ 1.01 and MacDraw 11/1.1 were used.

2.4. Rudis-Ek model ofspecies richness

The multiple regression model compiled by the authors is based on an analogous systems model of Rudis and Ek (1981 ). This study describes the forest land of Southeastem Wisconsin and was

160 M. Mikk, Ü. Mander / Landscapeand Urban Planning 31 (1995) 153-169

Table la Investigated parameters of woodlots in the Finnish study area

No. St A Ag D J T+I e

1 8 4.1 4 3 100 1.00 0.42 2 7 1.2 5 4 20 1.00 0.42 3 7 17.8 5 4 20 1.24 0.10 4 6 1 4 3 20 1.00 0.48 5 7 1.1 4 5 290 1.00 0.39 6 8 1.6 4 4 100 1.00 0.31 7 8 6.9 4 4 100 1.30 0.20 8 7 0.6 5 3 120 1.00 0.53 9 8 4.4 3 5 10 1.41 0.30

10 7 3.5 5 4 10 1.00 0.21 II 8 6.4 4 5 10 1.28 0.19 12 6 15.6 5 4 60 1.48 0.11 13 8 14.4 5 4 20 1.45 0.12 14 7 0.9 2 4 220 1.00 0.50 15 9 20.5 5 3 15 1.41 0.12 16 7 0.2 1 3 10 1.00 1.40 17 7 0.3 4 3 10 1.00 1.17 18 5 7 5 4 15 1.00 0.23 19 7 15.6 5 4 15 1.45 0.13 20 6 1 5 4 30 1.00 0.49 21 7 2.1 3 4 15 1.00 0.41 22' 8 8.5 5 3 10 1.24 0.26 23 8 13.7 5 4 10 1.30 0.10 24 7 0.4 4 4 10 1.00 0.78 25 9 2.5 5 3 10 1.00 0.43 26 7 4.9 4 3 20 1.00 0.23 27 8 25.7 5 4 15 1.60 0.16 28 8 11.8 5 4 15 1.00 0.15 29 7 2.4 4 3 30 1.00 0.34 30 7 1.6 4 4 15 1.00 0.44 31 7 0.1 2 3 15 1.00 1.60 32 6 1.7 4 5 70 1.00 0.41 33 7 3.7 4 3 10 1.00 0.21 34 7 9.8 4 3 10 1.30 0.15 35 7 4.9 5 4 15 1.30 0.20 36 8 1.7 4 3 15 1.00 0.36 37 5 0.5 3 3 310 1.00 1.40 38 7 25 5 4 30 1.65 0.13 39 6 0.2 4 3 30 1.00 1.10 40 7 0.8 4 4 20 1.00 0.50 41 6 6.6 5 4 15 1.00 0.21 42 5 2 4 4 15 1.00 0.47

No., number of the wood1ot (see Fig. 2(a»; St, number oftree and shrub species; A, area (ha); Ag, age class 1-5; D, disturbance class 1-5; J, distance to the nearest woodlot (m); T + 1, biotope heterogeneity (determined using Shannon-Wiener index); e, edge index (m per 10 m2

); Sh, number ofherb species.

M. Mikk, Ü. Mander / Landscapeand Urban Planning 31 (1995) 153-169 161

Table lb Investigated parameters of woodlots in the Estonian study area

No. St Sh A Ag D W 1 T+l e

1 10 29 3.4 2 4 3 330 1.48 0.26 2 13 34 6.8 3 4 3.5 240 1.68 0.15 3 10 27 0.3 5 2 3.5 470 1.00 0.70 4 10 19 0.8 3 2 2.5 390 1.28 0.56 5 12 21 0.9 3 2 2.5 180 1.00 0.36 6 17 50 14.4 4 4 4 20 1.70 0.24 7 8 24 0.8 2 4 3 90 1.48 0.59 8 10 33 0.4 2 3 2.5 40 1.60 0.78 9 15 38 2.7 2 4 3 20 1.83 0.64

10 10 32 2.2 2 2 2 130 1.71 0.31 Il 9 29 0.2 4 3 3.5 100 1.00 1.00 12 12 29 0.2 3 2 2.5 100 1.00 0.95 13 9 25 0.5 2 3 2.5 110 1.58 0.54 14 4 26 0.9 3 2 2.5 15 1.00 0.60 15 13 33 0.5 4 3 3.5 15 1.70 0.62 16 Il 30 1.5 2 4 3 70 1.70 0.43 17 10 25 0.8 2 3 2.5 260 1.60 0.48 18 9 31 0.4 3 4 3.5 140 1.00 0.93 19 6 15 0.2 2 2 2 60 1.00 1.00 20 10 29 0.4 4 3 3.5 60 1.00 0.67 21 13 63 16.1 3 4 3.5 80 1.28 0.19 22 9 46 0.7 2 4 3 110 1.28 0.41 23 8 16 0.1 3 3 3 80 1.28 1.10 24 6 23 0.2 3 3 3 80 1.70 1.00 25 9 33 0.6 3 3 3 90 1.30 0.52 26 7 19 0.5 2 3 2.5 25 1.48 0.78 27 13 33 0.2 3 4 3.5 25 1.68 0.22 28 10 23 0.8 3 2 2.5 110 1.44 0.58 29 Il 18 0.7 2 2 2 15 1.30 0.68 30 9 33 0.9 2 2 2 15 1.00 0.58 31 5 18 0.7 2 2 2 15 1.28 0.70 32 7 26 1.4 2 2 2 15 1.00 0.49 33 Il 35 0.9 4 1 2.5 320 1.48 0.46 34 5 19 0.2 4 2 3 320 1.30 1.06 35 7 20 0.6 4 3 3.5 70 1.00 0.47 36 11 34 7.5 3 4 3.5 15 1.30 0.18 37 Il 27 13.3 3 4 3.5 15 1.00 0.11 38 15 50 28 5 3 4 40 1.68 0.11 39 9 36 1.1 2 4 3 40 1.00 0.50 40 12 54 26.8 5 3 4 15 1.72 0.12 41 8 25 1.8 4 3 3.5 10 1.28 0.89 42 Il 26 4.1 5 3 4 15 1.58 0.27 43 6 16 0.4 4 3 3.5 10 1.30 0.75 44 Il 30 3.5 4 4 4 15 1.42 0.24

See Table la footnote.

162 M. Mikk. Ü. Mander / Landscapeand Urban Planning 31 (1995) 153-169

Table le Investigated parameters of woodlots in the Lithuanian study area

No. St A Ag D 1 T+l e

1 12 0.2 3 3 80 1.48 1.00 2 14 0.2 4 2 80 1.30 LlO 3 15 0.8 4 4 50 1.30 0.64 4 12 0.3 3 4 40 1.48 0.73 5 10 0.2 3 4 40 1.30 1.00 6 9 0.1 3 4 30 1.48 1.40 7 10 0.5 3 3 30 1.48 0.60 8 11 0.2 4 2 90 1.48 0.90 9 6 0.4 3 4 150 1.30 0.60

10 10 0.8 3 4 80 1.30 0.33 Il 15 0.4 3 4 40 1.48 0.98 12 8 0.1 3 2 30 1.30 LlO 13 12 0.3 3 3 30 1.00 1.00 14 14 0.8 4 3 15 1.45 0.50 15 Il 0.8 4 4 15 1.30 0.63 16 10 0.1 2 3 30 1.28 1.30 17 16 4.2 4 4 50 1.89 0.27 18 11 2 5 4 40 1.30 0.34 19 8 0.4 4 2 70 1.30 0.68 20 11 0.4 4 4 15 1.70 0.78 21 9 0.2 4 4 15 1.30 1.00 22 12 0.8 4 4 40 1.48 0.50 23 10 0.2 5 4 20 1.00 0.95 24 10 0.3 4 4 70 1.30 0.80 25 12 1 3 4 130 1.60 0.68 26 5 0.1 2 2 40 1.30 1.40 27 9 0.4 2 3 40 1.48 0.88 28 9 0.3 5 3 10 1.30 1.03 29 10 0.2 5 2 10 1.30 1.05 30 Il 0.2 4 3 20 1.60 1.05 31 Il 2 4 4 20 1.60 0.30 32 9 1.7 4 4 25 1.60 0.29 33 13 4.7 4 4 25 1.68 0.40 34 8 0.5 3 4 140 1.48 0.64 35 11 2.9 3 4 60 1.80 0.42 36 5 1 3 4 25 1.45 0.46 37 5 0.1 3 4 30 1.30 1.00 38 9 0.2 2 4 25 1.48 0.90 39 6 0.2 3 2 60 1.48 1.00 40 10 1.2 5 3 80 1.48 0.50 41 8 0.6 4 3 80 1.30 0.60

See Table la footnote.

M. Mikk. Ü. Mander / Landscape and Urban Planning 31 (1995) 153-169 163

Table 2 Quantification ofthe values for the combined age and disturbance coefficient ( W)

Disturbance level Age (years)

<20 21-40 41-60 61-80 >81 (1 ) (2) (3) (4) (5)

Very high (1) 1.0 1.5 2.0 2.5 3.0 High (2) 1.5 2.0 2.5 3.0 3.5 Moderate (3) 2.0 2.5 3.0 3.5 4.0 Low (4) 2.5 3.0 3.5 4.0 4.5 Verylow (5) 3.0 3.5 4.0 4.5 5.0

Figures in parentheses refer to the numbers of age classes and disturbance levels (see Tables 1 (a)-l (c».

made for finding out the optimallandscape pat­tern to preserve species.

The Rudis-Ek model was especially designed to investigate tree species diversity as a function of islands' characteristics. Variables used in the systems model represent those which were pri­marily available from remote sensing data. The study was not supported by field studies. Rudis and Ek considered that the number of forest spe­cies varies with area, topography, successional age, degree of disturbance, interactions with other islands, and changes in these elements over time.

The equation used by Rudis and Ek (1981) was

where Sit is species richness on island i, Ai is the area of island i, Ti is the relief or general soil con­ditions of island i, Wi is successional age and de­gree of disturbance of island i, Ii is the interac­tions term relating the exchange of propagules, from another island j to island i, ris time aver­aged changes affecting island i since its establish­ment, ei is the error term, and bl> b2 , b3 , and b4

are constants. This model contains parameters which, in

practice, are not easy to find, especially the time averaged changes ofthe forest islands since their establishment. Likewise, it is a complicated task to fit into the model the measurable criteria for interaction terms relating to the exchange of pro­pagules between various forest islands (see also Mader, 1984). Therefore, a simpler analogous

model has been compiled based on the multiple regression analysis of variables.

3. Results and discussion

3.1. M odel parameters

To compile a model for estimation of species richness (Si) of the ith forest island within the agricultural fields, the following parameters have been used: area (ha) of woodlot (Ai)' distance to the nearest woodlot (m) as the isolation fac­tor (li), the heterogeneity coefficient (Ti)' the coefficient of age and disturbance (Wi ), and the edge index (ei)'

The heterogeneity coefficient (Ti) has been calculated on the basis of soil maps at the scale of 1:5000 (in Estonian study area) and 1:10 000 (in Lithuanian study area), or forest site type maps at the scale of 1 :20 000 (in Finnish study area). For the calculation of this coefficient the Shannon-Wiener index of diversity (Pielou, 1969) was used. For the individual forest island the formula is

Si

Ti = L (nij/N;)ln(nij/Ni ) j

(2)

where Ti is the index of biotope (i.e. soil cover) diversity within the ith wood1ot, Si is the number of soil types within the ith wood1ot, nij is the numberofindividual soil units ofthejth soil type within the ith woodlot, Ni is the total number of individual soil units within the ith wood1ot. In

164 M. Mikk. Ü. Mander / Landscape and Urban Planning 31 (1995) 153-169

the Finnish study area instead of soil units, for­est site types have been used. In the model, the parameter size Ti+ 1 will be used (see also Table 1 ).

The coefficient of age and disturbance (Wi )

has been compiled on the basis of a five-step rel­ative scale both for the age and disturbance level of wood patches, and is shown in Table 2.

Disturbance levels have been estimated only for the local conditions and are defined as follows.

(1) Very high: close (less than 10 m) to the intersections of roads, close to large barns (less than 50 m), heavy deposition ofammonia, solid waste dumping sites on the borders, immedi­ately adjacent to open pit quarries of gravel or sand (less than 100 m).

(2) High: with the same disturbance factors of lower quantities (distance 20-200 m), adjacent to the farmsteads.

(3) Moderate: disturbance factors lower than in the last group, mostly disturbed by solid waste from local farmsteads.

(4) Low: lying at least 0.3 km from big roads and 0.5 km from the farmsteads.

(5) Very low: lying at least 0.6 km from big roads and 1.5 km from the farmsteads.

In the Estonian and Lithuanian study areas no woodlots with very low disturbance level were found. In Estonia one woodlot was evaluated as very heavily disturbed.

The edge index (ei ) has been calculated on the basis ofland-use maps (1: 10 000) as the border­line length of the ith woodlot (li; m) per area (Ai; m2

) of the woodlot: ei= 10(IJAi)'

3.2. Regression analysis

Linear regression analysis has been used to characterize the relationship between tree and herb species and other recorded parameters of woodlands. The correlation coefficients between variables are shown in Tables 3-5. Different pa­rameters vary significantly in their influence on the species richness. There are also clear differ­ences of this influence in different study areas. So in Estonia the woodlot area has been found to be the most essential factor in influencing the tree

and shrub species richness (r=0.53; Fig. 4(b». As expected, a high correlation was found be­tween the area and biotope heterogeneity (r=0.45). The correlation between the age and species richness, and the level of disturbance and species richness in particular was clearly lower (r=0.38 and r=O.22, respectively). It is inter­esting to point out that no significant correlation was found between the degree of isolation (dis­tance to the nearest woodlot) and species diver­sity (r= 0.04 ). That is rather typical for the pres­ent Estonian agricultural landscapes where the percentage of forested areas is normally more than 20-25% and the distances between the woodlands are normally less than 0.5 km but never exceed 3 km.

In the case ofherb species of the Estonian study area an interesting relationship should be men­tioned, namely the correlation between the level of disturbance and species richness (r=0.42) which was found to be higher than the correla­tion between species richness and biotope heter­ogeneity (r=0.31). It probably demonstrates the influence of nitrogen deposition in small wood­lots. They receive a lot of ammonium from the adjacent barns and manure heaps. Likewise, the influence of area on species richness was higher in the case ofherb species than in the case oftrees and shrubs (r= O. 70). As expected an even higher correlation value (r=O.72) was found when analyzing the influence of area on the total num­ber of vascular species in woodlots (Fig. 4 ( d) ).

In the Finnish study area, the values of corre­lation coefficients between parameters were rel­atively low. Only the biotope heterogeneity was significantly correlated with the area of the woodlots (r=0.86; P<O.Ol). Seemingly, this could be a result of the lower patchiness of forest site types (in this case, soils) in the Finnish study area. Also, the most essential factor determining the species diversity of woodlots in Finland was biotope heterogeneity (r= 0.27) but not the area (Table 3; Fig. 4(a».

In Lithuania, the most significant factor influ­encing the tree and shrub species richness of woodlots was the woodland area (r=0.38; Fig. 4 ( c ) ). The next most significant factors for spe-

M. Mikk. Ü. Mander 1 Landscapeand Urban Planning 31 (1995) 153-169

Table 3 Correlation coefficients (r) for variables ofwoodland patches in the Finnish study area

Variable 2 3 4 5

Number oftree and shrub species (St) 1 1.00 Area (A) 2 0.24 1.00 Age (Ag) 3 0.17 0.51* 1.00 Disturbance (D) 4 -0.23 0.14 0.13 1.00 Distance to the nearest woodlot (1) 5 -0.19 -0.24 -0.17 0.02 1.00 Biotope heterogeneity (T + 1 ) 6 0.27 0.86- -0.07 0.23 0.05 Edge index (e) 7 -0.29 0.57** 0.68- 0.39* 0.24

* P<0.05; -P<O.OI.

Table 4 Correlation coefficients (r) for variables of woodland patches in the Estonian study area

Variable 2 3 4 5 6 7

Number oftree and shrub species (St) 1 1.00 N umber of her species (Sh) 2 0.65- 1.00 Total number of plant species (Sth) 3 0.78- 0.97- 1.00 Area (A) 4 0.53* 0.70- 0.72- 1.00 Age (Ag) 5 0.22 0.21 0.24 0.42* 1.00 Disturbance (D) 6 0.38* 0.42* 0.44* 0.27 0.27 1.00 Distance to the nearest woodlot (1) 7 -0.04 -0.14 -0.13 -0.20 0.01 -0.28 1.00 Biotope heterogeneity (T + 1 ) 8 0.45* 0.31* 0.37* 0.29 0.05 0.29 -0.06 Edge index (e) 9 0.55* 0.50* 0.55* 0.56** -0.04

* P<0.05; -P<O.01.

Table 5 Correlation coefficients (r) for variables ofwoodland patches in the Lithuanian study area

Variable

Number of tree and shrub species (St) Area (A) Age (Ag) Disturbance (D) Distance to the nearest woodlot (I) Biotope heterogeneity (T + 1 ) Edge index (e)

* P<0.05; -P<O.01.

1 2 3 4 5 6 7

1.00 0.38* 0.29 0.18

-0.09 0.28

-0.25

2

1.00 0.22 0.37*

-0.02 0.58-0.71-

3 4

1.00 -0.01 1.00 -0.17 -0.02 -0.07 0.23

0.32* 0.46*

0.32* 0.04

5

1.00 0.05

-0.17

165

6 7

1.00 0.47* 1.00

8 9

1.00 -0.37 1.00

6 7

1.00 0.42* 1.00

cies diversity were age and biotope heterogeneity (r=0.29 and r=0.28, respectively).

and other variables (Wj , Tj+ 1, Ij) was linear.

3.3. Model compilation and verification

To compile the multiple regression model for vascular plant species in forest islands (Sj) the species-area relationship used was non-linear. The relationship between the number of species

A multiple regression model for vascular plant species (Sj) in woodlots was compiled on the ba­sis of this study

Sj= (SIlO) [clAP +C3 Wj

+c4(Tj + 1) +c5 (1IIf6)] (3)

where Sj is species richness in the ith wood patch,

166 M. Mikk, Ü. Mander / Landscape and Urban Planning 31 (1995) 153-169

(a)

y ~ 0.497LOG(x) + 6.852 R2~O.107

0 0

0 rn rn 0 0 00 0 0

7ro 0 0 0 0

6 00 0 0

5 b 0 0

4 0 10 15 20 25 30

Area (ha)

(b)

R 2 = 0.321 Y = 2.557LOG(x) + 9.733 20.----------------~,..___-____,

0

0

0 0

O+---.---.---.---.---,--~ o 10 15 20 25 30

Area (ha)

(c) 20

~

.'!! " [5 0 0

" 0 0 ~

-5\ 00 00 00

Ë 10 00 00

"0 000 t: <0

1000 " 5 0 ::: E-

0 0

(d) 80

en 0)

'ü 70 0)

5l' -e 60 0)

0 ..c: 0 "0 50 t:

'" .g 40 BD Ë cU 30

::: E-

0

00

y = 2.312LOG(x) + 10.980 R2= 0.159

o o

o o

Area (ha)

o

o

y = 13.613LOG(x) + 38.866 R2 = 0.486

o o 00

o

10 15 20 25

Area (ha)

30

Fig. 4. Species-area regression for wood patches: (a) tree and shrub species in Finland; (b) tree and shrub species in Estonia; (c) tree and shrub species in Lithuania; (d) total number of vascular species in Estonia.

Sis the average number of corresponding species in the study region, Ai is the area of the woodlot (m2 ), Wi is a combined age and disturbance coefficient ofisland i, Ti + 1 is biotope heteroge­neity of island i (determined on the base of soi! data or forest site types using the Shannon-Wie-

70.,-------::---------------, y = 0.568, + 16.727 R 2 = 0.570

00 60 o

"0 o

'" 0 0

~ 50 0 0 0

ü 0

-;: 40 U 0

oo%dl 0

30 0 0

0 0

20 20 30 40 50 60 70 80

Recorded

Fig. 5. Regression between the measured and calculated number of vascular plant species in the Estonia study area.

ner index), Ii is the isolation coefficient (dis­tance to the nearest woodlot; m), and Cl> C2, C3,

C4 , C5' and C6 are multiple regression coefficients. Compared with the Rudis-Ek model, a prin­

cipal new component in the model is the average species number ofthe region. For instance, in the Estonian study area, the average number of tree and shrub species in a medium size forest ecosys­tem has been counted as ten species. In Finland and Lithuania this number is 7 and Il, respec­tively. The average regional number ofherb spe­cies in woodlands of the Estonian study area has been counted as 30 species.

As there was a very low correlation between species richness and distance to the closest wood patch, the isolation factor was not included in the final version of the model (Eq. (4». Likewise, several researchers have mentioned the low sig­nificance of the isolation factor on species rich­ness in forest islands within the matrix of agri-

M. Mikk, Ü. Mander / Landscapeand Urban Planning 31 (1995) 153-169 167

cuiturally used lands (Hobbs, 1988; Van Ruremonde and Kalkhoven, 1991). The effect of isolation may be more important when spe­cies are analyzed separately as is done in the me­tapopulation approach (Ouborg, 1993). Also, in the case of a really poor agricultural landscape with very large distances between woodlands, the importance of the isolation factor increases. The same occurs when different organisms are stud­ied (i.e. birds, spiders, carabid beetles; Mader, 1984; Werres, 1984).

Because of relatively low correlation values between tree and shrub species richness and other variables studied, the multiple regression analy­sis gave non significant results for Finnish and Lithuanian study areas. Thus, only for the Eston­ian study area did the model parameters fit re1a­tively weIl. For all vascular plant species, in par­ticular, the correlation between calculated and recorded species was found to be relatively high (Fig. 5). The compiled model describes 57% of the real tree, bush and herb species richness in the current study area. In this case the model with calculated parameters is

(4)

where Sth i is tree, shrub and herb species rich­ness on island i.

Besides purely theoretical importance, such a modeling approach has some relevance to ap­plied problems of nature conservation and land­scape planning. One of them is the optimal size and shape of ecosystems as potential reserves.

3.4. Optimal size and shape of woodlots

According to the classical point of view, to guarantee the maximum possible species rich­ness, nature protection areas should be as large and compact as possible (Buckley, 1982; Schnida and Wilson, 1985). Some authors, however, have found the opposite, especially taking into ac­count the whole spectrum of habitat variety (Higgs and Usher, 1980; Jarvinen, 1982). Ow­ing to differences in past and current manage­ment and also micropatchiness, small habitats could be more valuable than comparable areas forming part oflarger blocks ofwoodlands (Hel-

liwell, 1976). In further contrast to the classical theory of island biogeography, Zacharias and Brandes (1990) pointed out that on average a single wood contains less species than two smaller woods of the same total area.

On the basis of our case study areas, it is diffi­cult to say something significant about the con­servation value of the ecosystems studied. Nor­mally, within a small territory, like our study areas, a large variety of species, especially rare ones, does not occur. However, one of the meas­urable parameters often used by ecologists to characterize the differences of the regional spe­cies diversity, gamma-diversity (Whittaker, 1977), showed relatively large variations be­tween the study areas. For instance, tree and bush species richness is decreasing significantly from south to north: the values of gamma-diversity were 0.82 ha- I in Lithuania, 0.17 ha- I in Es­tonia, and 0.05 ha -1 in Finland.

From the point ofview of nature conservation and landscape planning, the whole spectrum of various habitats is valuable and should be main­tained (Forman and Godron, 1981). This is one ofthe basic concepts for the environmental man­agement of territories. In different countries, various terms will be used to describe this hier­archical system of ecologically valuable areas (i.e. network of the habitat connection areas, ecolog­ically compensating areas etc. ). In the future the European Ecological Network (EECONET) will serve as the basis for environmental policy in Europe (Bischoff and J ongman, 1993).

4. Conclusions

The MacArthur-Wilson theory of island bio­geography is difficult to apply to agricultural landscapes with the isolated pattern of naturalj semi-natural ecosystems. First, the surrounding matrix of agriculturalland is not a very signifi­cant isolation factor for species dispersion. Sec­ondly, several additional factors (e.g. age and disturbance of ecosystems, biotope diversity) as well as the classical ones like area and isolation rate, play an essential role in developing species richness.

168 M. Mikk, Ü. Mander / Landscapeand Urban Planning 31 (1995) 153-169

However, a relatively good correlation was found between the number oftree and shrub spe­cies and the area ofwoodlots in the Estonian and Lithuanian study areas (R 2 =0.28 and 0.14, re­spectively, P<0.05). In the study area of south­ern Finland with intensively managed wood­lands the correlation was not significant (R 2 =0.06). When considering all vascular plants (Estonian study area) the area-species correlation was clearly higher (R 2 =0.52).

The multiple regression model of species di­versity compiled by the authors for Estonian conditions, describes about 57% of the real vas­cular plant species richness of woodlands. For the Finnish and Lithuanian study areas no repre­sentative model could be compiled because oflow correlation values between the variables.

The predominance of nitrophilous plants in the herb layer of all woodlots in the Estonian study area indicates significant disturbance of nutrient cycling in small forest islands. One of the reasons for such disturbance is a slightly higher nitrogen ( especially ammonia) deposition in the vicinity of farm complexes or fields with intensive fertilization.

Further investigations of woodland patches and other isolated biotopes within the matrix of agriculturalland should be linked to the ecolog­ical network of the territory. This is a hierarchi­cal system of naturalj semi-natural ecosystems (forests, wetlands, natural grasslands, coastal waters, etc.) compensating the anthropogenic load of the area. Isolated woodland patches are only the part of the network that influence spe­cies diversity at the local level. At the regional level, the large-scale pattern of natural ecosys­tems plays a more essential role. During the long­term development in Estonia and neighbouring countries an optimal ecological network of com­pensating ecosystems has been formed (Mander et al., 1988). At present, during privatization this network should be maintained.

Acknowledgments

We wish to thank Dr. Juha Helenius from the Department of Applied Zoology, University of

Helsinki for the support in field work in the Fin­nish study area, Ona Balciunaite and Dr. Zen­onas Gulbinas from the Institute of Geography, Vilnius, Lithuania for helping us to organize field investigations in Lithuania. We also thank Jaa­nika Kerna and Tonu Mauring for helping with field investigations, as well as Dr. Tonu Oja from the Institute of Geography, University of Tartu, Estonia for critically reading the manuscript.

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