Landscape Ecol (2009) 24:1105–1121 DOI 10.1007/s10980-008-9274-z
RESEARCH ARTICLE
The synergistic effect of combining woodlands and green veining for biodiversity Carla J. Grashof-Bokdam Æ J. Paul Chardon Æ Claire C. Vos Æ Ruud P. B. Foppen Æ Michiel WallisDeVries Æ Marja van der Veen Æ Henk A. M. Meeuwsen
Received: 2 June 2008 / Accepted: 11 September 2008 / Published online: 18 November 2008 Ó Springer Science+Business Media B.V. 2008
Abstract Combining nature reserves with small semi-natural elements (green veining) may improve the persistence of plant and animal species in fragmented landscapes. A better understanding of this synergy is essential to improve species diversity in the European Natura 2000 sites and in green veining elements. To test this hypothesis, we investigated the relationship between the occurrence of 40 forest plant and animal species in 1,000 km grid cells in the Netherlands and the spatial cohesion of the surrounding large woodlands and small woody elements. Two types of synergy were found. First, nine species were more often present if there was more cohesion of large elements; small elements enhanced
Electronic supplementary material The online version of this article (doi:10.1007/s10980-008-9274-z) contains supplementary material, which is available to authorized users. C. J. Grashof-Bokdam (&) J. P. Chardon C. C. Vos M. van der Veen H. A. M. Meeuwsen Alterra, Landscape Centre, Wageningen University and Research Centre, P.O. Box 47, 6700 AA Wageningen, The Netherlands e-mail:
[email protected] R. P. B. Foppen SOVON: Dutch Centre for Field Ornithology, Rijksstraatweg 178, 6573 DG Beek-Ubbergen, The Netherlands M. WallisDeVries Dutch Butterfly Conservation, P.O. Box 506, 6700 AM Wageningen, The Netherlands
this effect. Second, 11 other species were more often present when there was more cohesion of small elements; large elements enhanced this effect. Eight species showed both effects, indicating two-way synergy. The remaining 12 species preferred landscapes dominated by either large or small elements, or displayed no positive relationship whatsoever to woody elements. Species showing synergy often had a low dispersal capacity; the type of synergy seemed to be related to their habitat preference. These results imply that species diversity could be improved by integrating different policy instruments used for nature reserves and green veining. Using a zoning principle where green veins surround and connect nature reserves, the different spatial and habitat preferences of species can be secured. In this way a coherent network could become reality. Keywords The Netherlands Forest habitat Ecological networks Connectivity Species occurrence Regression analysis Dispersal capacity Habitat area requirements Habitat preference Landscape planning
Introduction The increase in the human use of natural resources in the twentieth century has led to fragmentation of habitats where nature remnants exist within a matrix
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of intensive agriculture or urban area. This resulted in loss of biodiversity. It has been widely recognized that landscape-scale conservation objectives, where ecosystem networks are protected instead of isolated patches, are a key conservation strategy (Bennett 2004; Jongman et al. 2004). In Europe for instance, a landscape scale network of nature reserves, the Natura 2000 network, has been developed as an answer to the ongoing process of habitat fragmentation. It consists of Special Areas of Conservation (SAC) designated by member states under the Habitats Directive, and also incorporates Special Protection Areas (SPAs) which are designated under the 1979 Birds Directive (European Commission 2008). Other examples of international ecological networks of nature reserves are the tri-national network of protected areas in the Congo region, the multifunctional New England Greenway located in six states along the East Coast of the USA (Bouwma et al. 2003) and the Atlantic Forest network of rainforest remnants in Brazil, Paraguay and Uruguay along the Atlantic coast (Conservation International 2008). However, although many Natura 2000 sites have been allocated by now, the sites do not function as a coherent network yet. Two main factors constrain the ecological functioning of the Natura 2000 network. First, the individual areas of the Natura 2000 network are too isolated from each other to ensure a sufficient exchange of individuals to guarantee the long-term survival of metapopulations (Hanski and Gilpin 1991; Opdam and Wiens 2002). Given the importance of patch size and connectivity for species survival in ecological networks (as demonstrated, for instance, by Debinsky and Holt 2000 and Bowne and Bowers 2004), the spatial cohesion of the Natura 2000 network needs to be improved. Spatial cohesion reflects the total habitat area and the density of habitats in a network, where total habitat area reflects survival probability of the metapopulation and habitat density reflects the dispersal stream across the network (Opdam et al. 2003). A second factor inhibiting the functioning of the Natura 2000 network is the expansion and intensification of land use that has resulted in an increasingly hostile matrix (Donald and Evans 2006). Intensive land use in the landscape matrix (i.e. the non-habitat surrounding suitable habitat, Wiens 1995) impedes dispersal for many species (Ricketts 2001; Vos et al. 2002). It was shown that habitat patches linked by
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landscape structures that are permeable to dispersers are more often occupied (e.g. Dunning et al. 1995; Gonzalez et al. 1998), have a higher exchange rate of individuals (e.g. Haas 1995; Haddad 1999; Tewksbury et al. 2002) and have higher population densities (Haddad and Baum 1999). Thus improving matrix permeability will contribute to the functional connectivity (Taylor et al. 1993; With et al. 1997) of the Natura 2000 Network. Green veining (Grashof-Bokdam and Van Langevelde 2004; Billeter et al. 2008) can play a role in solving these two constrains. Green veining is the network of small-scale semi-natural elements like field margins, road verges, ditch banks, hedgerows and small woodlots that surround agricultural fields and is a part of the matrix landscape that surrounds nature reserves. In the mosaic approach, habitat patches in nature reserves and in the matrix are considered together (Williams et al. 2006; Fisher et al. 2005; Murphy and Lovett-Doust 2004; Ricketts 2001). It has been found that green veining around nature reserves provides (suboptimal) habitat for several bird species (Foppen et al. 2000). In this way, green veining can support the (meta)populations in Natura 2000 sited by offering (temporary) habitat. Furthermore, these green veins increase matrix permeability by facilitating dispersal or foraging (Verbeylen et al. 2003; Luck and Daily 2003; Antongiovanni and Metzger 2005; Chardon et al. 2003; Vos et al. 2007). In this way, green veining contributes to the functional connectivity of the Natura 2000 network (Fig. 1). On the other hand, species in green veining may in turn benefit from proximity to Natura 2000 sites. Agro-environmental schemes, were farmers are financially stimulated to develop or maintain seminatural elements, are a EU policy instrument to increase the amount and the quality of these small scale networks in European agricultural landscapes. However, agro-environmental schemes do not always lead to improved species diversity in European green veining (Kleijn and Sutherland 2003). We expect that the vicinity of nature reserves may improve the effect of these schemes. Field studies have already demonstrated that for many species, populations in large habitat patches positively affect populations in small habitat patches (e.g. Verboom and Van Apeldoorn 1990; Foppen et al. 2000; Chardon et al. 2003; ¨ ckinger and Smith Mabelis and Chardon 2005; O 2007; Roy and de Blois 2008). This phenomenon is
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1. influx of colonizers
nature reserve
green veining
2. (temporary) habitat 3. higher matrix permeability
Fig. 1 Diagram representing the expected mutual benefits (synergy) from combining nature reserves and green veining: nature reserves support green veining by the influx of colonizing offspring (1), while green veining supports nature reserves by offering (temporary) habitat (2) and increasing matrix permeability by facilitating dispersal or foraging (3)
the result of source–sink processes, by which the populations in small patches are saved from extinction by the influx of colonizing offspring from source populations nearby (Pulliam 1988; Dias 1996) (Fig. 1). Several authors have demonstrated that species occurrence in green veins does indeed depend on nature reserves in the vicinity (Tscharntke et al. 1998; Delettre and Morvan 2000). This mutual benefit from nature reserves and green veining fits into the SLOSS debate (single large or several small). Where a single large patch is assumed to be superior compared to several small patches due to lower dispersal mortality and extinction rates (Verboom et al. 2001; Vos et al. 2001), in the case of a few large patches versus many small ones (FLOMS) (Etienne and Heesterbeek 2003; Ovaskainen 2002) many small patches may be better because here inter patch distances are smaller. In the same way a mix of linear, small and large patches as in a combination of nature reserves and green veining may be favorable compared to only large patches of nature reserves.
In view of the above, the species populations in a combination of small-scale green veining around large-scale nature reserves can be expected to be more resilient to changing land use and climate change (Bengtsson et al. 2003). We hypothesize that combining nature reserves and green veining leads to synergy, as it is a more effective way of protecting species diversity than implementing both networks separately. To effectively sustain species diversity, these networks must take account of the large variety of spatial scales at which species function (Vos et al. 2001). Perceiving landscapes from a species perspective, species with large individual area requirements and large dispersal capacity need extensive (possibly international) habitat networks. For small species, however, such as amphibians or butterflies, a local network of small ponds or woodlots combined with linear landscape elements may suffice (Verboom and Pouwels 2004). Besides spatial scale, it is plausible that the habitat function of large and small networks is also important. The most obvious synergy is when both networks comprise reproduction habitat. In that case, the combination of large and small elements in the landscape results in a larger and denser habitat network, which may improve species persistence. For barrier-sensitive species, the small elements may also increase matrix permeability, thus facilitating dispersal within the large network. A third potential benefit of combination exists if the small elements play a role in food collection for species that reproduce in the large network, thus improving food availability in the surroundings of large habitats (Tubelis et al. 2004). To test our hypothesis we analysed data on the distribution of forest plant and animal species in the Netherlands and tested whether their occurrence could be explained better by a combination of spatial cohesion of large and small woody elements rather than by the spatial cohesion either of large elements or of small elements. Furthermore, we evaluated which species traits regarding spatial scale (dispersal capacity and area requirements) or habitat functions of both networks (reproduction-, dispersal- of foraging habitat) are most important explaining possible synergy. The insights of this research may enable us to pronounce upon the added value of integrating the currently separated design and policy instruments regarding nature reserves and green veining for sustainable landscape planning.
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Study site and species We focused on woody habitat on the Pleistocene sandy soils in the eastern part of the Netherlands (Fig. 2a). The landscape in this area is a mosaic of forest patches and woody linear elements, usually embedded in agricultural land, but sometimes in urban areas. Forests comprise mostly deciduous forests, where silver birch (Betula pendula) and pedunculate oak (Quercus robur) dominate on the dryer and relatively nutrient poor soils (Quercetea robori-petraeae). On these soils, also coniferous forests (Vaccinio-Piceetea) have been planted, for instance on former heathlands. Here, pine (Pinus sylvestris), Larch (Larix decidua) and Spruce (Picea abies) stands mostly have a quite homogenous structure. Also mixed forests of deciduous and coniferous forest occur. In stream valleys, where soils are somewhat richer, less dry and more loamy, forests of sessile oak (Quercus petraea) and beech (Fagus sylvatica) occur (Querco-fagetea). Sometimes poplar stands have been planted, which mostly have a homogenous structure. Woody linear elements mostly consist of wooded banks and tree lines of pedunculate oaks on the dryer and nutrient poor soils that surround old fields. In stream valleys wooded banks and
Fig. 2 a Four regions (north, west, south, east) in the Netherlands forming the Pleistocene sandy soils, with example area in the north. b Detail of example area. Wooded habitat is divided into large elements (green) and small elements (orange). The cohesion value of large elements is indicated
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tree lines of common alder, poplar or willow trees surround old meadows and pastures.
Our study species met four criteria: (1) they use woody habitat for reproduction, foraging or as a conduit for dispersal, (2) they are neither very abundant nor very rare in the study area, because it is difficult to demonstrate significant effects using presence/absence data of very abundant or rare species. Rare species are present in \20% and abundant species in [80% of available km grids.
by darker grey indicating higher cohesion values. c As 2b, but now the cohesion value of small elements is indicated in grey. The cohesion values used have been calculated at a = 1.67, corresponding to a dispersal capacity between 1 and 3 km
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Most studied species are not protected by the habitator bird directive, because these species are often too rare for regression analysis. However, most selected bird and butterfly species are target species of the Dutch government. (3) they vary in species traits concerning spatial scale (dispersal capacity and habitat area required for reproduction), and (4) the reliability of the data on them was fair to good, according to the criteria on species identification applied in the management of the species databases that range from no data, poor, fair to good. Using these criteria 40 species were selected: 11 herbal plants, 15 birds and 14 butterflies (see Appendix— Electronic supplementary material). Data on their occurrence (presence/absence) in the selected 1,000 grid cells of 1 km2 were obtained from FLORON (FlorBase-2L), SOVON and Dutch Butterfly Conservation, respectively. Information on dispersal capacity and habitat area requirements for a key population were gathered from a database where species with comparable species traits concerning spatial scale are grouped into so called ‘ecoprofiles’ (Verboom and Pouwels 2004; Opdam et al. 2008). For this study, some ecoprofiles have been aggregated and some species have been shifted to adjacent ecoprofiles. A key population is defined as having a probability of extinction of\5% in 100 years if there is immigration from other patches (Verboom et al. 2001). Information on habitat function (reproduction, dispersal and foraging) was obtained from Bink (1992), Bos et al. (2006), SOVON (2002), CBS (1997), Weeda et al. (1994) and using expert judgement.
1109
Greater Stichwort (Ó Ruut Wegman)
Speckled Wood (Ó Ruut wegman)
Data and methods Definition of large and small woody elements
Bullfinch (Ó Shutterstock)
We used one definition of large and small woody elements and one set of habitat data for all studied species. First, we constructed a habitat map in which all woody habitats on the Pleistocene sandy soils were defined as either small or large elements. For this purpose we used land use data for 25 9 25 m grid cells from the national VIRIS-grid database. From this database we selected the land use classes deciduous forest, coniferous forest, mixed forest and poplar forest. Mixed forest comprises \80% either deciduous or coniferous forest. Complexes of contiguous 25 m2 grid
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cells of forest habitat of all forest types larger than 5 ha and wider than 50 m were defined as large elements. The 5 ha criterion is based on the Dutch schemes for nature management that set a minimum area of 5 ha for nature areas. The remaining contiguous complexes of small woodlots were defined as small elements. To this grid map we added woody linear and point elements from a 1:10,000 scale vector map, We assigned widths of 3 and 4 m respectively to hedgerows and lines of trees, and an area of 16 m2 to each solitary tree. All linear and point elements were defined as small elements. Selection of km2 cells Next, on the Pleistocene sandy soils 1,000 grid cells of 1 km2 were selected for the regression analysis, using three criteria: the cell should have reliable species data (see species data); the cell should contain no more than 50% urban area and/or water; and the cell’s distance from the German or Belgian border should be at least 3 km. In the cells meeting these criteria, the areas of large and small elements had to have a wide range, in order to obtain accurate estimates of their effects while retaining representativeness. Therefore the remaining cells were classified into 12 strata according to the % of area of large elements (0, 0–20, 20–60 and[60%) and to the % of area of small elements (0–5, 5–10, [10%) within the bordering km grid cells. Then 100 stratified random samples of size 1000 were taken from these 12 strata. The random sample with the smallest determinant of the 4 9 4 covariance matrix of the four variables area large, (area large)2, area small and (area large 9 area small) was chosen (Montgomery et al. 2001). Variables of habitat quality and spatial configuration As species occurrence is determined not only by the spatial configuration of habitat but also by habitat quality, several habitat quality factors were defined for each km grid cell. We calculated the area (m2) of each soil type present (clay, sand, peat, loam, plaggen soils), area of urban/water land use, areas of land with different water tables (wet, humid and dry) and maximum forest age. We added the sub-region of the grid cell in question (north, west, south, east: see Fig. 2a) as a geographic variable. Sub regions are separated from each other by larger river beds. Most
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of the selected study species do not occur evenly in these more or less isolated sub regions. Spatial cohesion was calculated for each species and each grid cell of 25 9 25 m and was calculated separately for large elements and for small elements. It comprised the area of woody habitat in the surroundings of the grid cell, corrected for the distance to the cell. ‘‘Surroundings’’ is defined as all other grid cells surrounding the grid cell under study that lie within the maximum dispersal capacity of the species concerned: it varied from 461 m for species with a dispersal capacity ranging from 0 to 1 km, to almost 30 km for species with a dispersal capacity exceeding 25 km (Table 1). We assumed that distant habitat contributes less to the probability of occurrence of a species (Cozzi et al. 2008). Woody habitat beyond the species dispersal range will no longer contribute to the cohesion of a particular grid cell. For the calculation of cohesion values we used the LARCH-SCAN model (Groot Bruinderink et al. 2003), which calculates spatial cohesion for each grid cell i (Ci) as the sum of the area (Aj) of all woody habitat j in the surrounding grid cells, corrected for the distance (dij) from each habitat element to the grid cell in question (Fig. 2b, c). The constant as indicates the steepness of this decrease (Hanski 1994) and thus the maximum distance from woody habitat to the central grid cell at which habitat still contributes to the cohesion value of that cell. This distance corresponds to the maximum dispersal capacity of the species in question (see Table 1). Ci ¼
n X
Aj ðeÞas dij
j¼1
Finally, the cohesion of a km grid cell was calculated as the mean value of all cohesion values Table 1 Used values of a for different classes of dispersal capacity of the selected 40 study species, indicating the maximum distance from woody habitat to the central grid cell at which habitat still contributes to the calculated cohesion value of that cell Value of a 5.0
Maximum distance (m)
Dispersal class (km)
461
0–1
1.67
1,379
1–3
0.45
5,116
3–7
0.2
11,513
7–15
0.12
19,188
15–25
0.077
29,904
[25
Landscape Ecol (2009) 24:1105–1121
of the 1,600 25 9 25 cells containing woody habitat within this km grid cell. CL is the mean cohesion value for large elements and CS the mean cohesion value for small elements. Statistical analysis For each species, we related the presence–absence data in all km grid cells to habitat and spatial variables by a logistic regression analysis. First, we fitted all habitat quality variables as a correction factor, using a forward selection. All habitat quality variables showing a significant effect in this forward selection were used as a null model in the next step. Then, the cohesion variables CS and CL were added to the null model separately; subsequently, they were added to the null model together. Finally, the interaction term CS * CL was added. A significant positive interaction implies that a combination of small and large elements leads to a higher occurrence probability than would be expected when only the main effects of small and large elements are taken into account. For this analysis a logistic regression analysis was carried out (McCullagh and Nelder 1989; Jongman et al. 1995), using the GENSTAT statistical package (Genstat 5 Committee 1993). Next, using the regression results, we predicted the probability of species occurrence of each species as a function of spatial cohesion of small elements (CS) for the minimum, mean and maximum cohesion of large elements (CL). In the same way we predicted the occurrence of each species as a function of the spatial cohesion of large elements (CL) for the minimum, mean and maximum cohesion of small elements (CS). Using these curves of predicted probabilities of occurrence, we classified all species into distinct types of response to the cohesion of large and small elements.
Results First, we will give an overview of the effects of the habitat quality variables used in the regression analysis and of the values of cohesion of large and small elements found in the selected km grids. Next, we distinguish seven response types (Table 2), according to the response curves of predicted probability of occurrence versus cohesion of small and
1111
large elements. For each response type, we will show, per species, the effects of cohesion of small elements (CS) and of cohesion of large elements (CL), when added to the regression model separately and together. The effect of the interaction term of both variables (CL * CS) is also presented. Species with significant interaction terms are interpreted as showing synergy significantly and their results will be related to dispersal capacity and area requirement (Table 3) and to habitat function (Table 4). For most species ‘region’ had a significant effect on occurrence probability (Table 2). For many species, there were also significant effects from areas of urban land use and of water (i.e. the non-habitat in the landscape) in the null model. Finally an important quality variable for many species was the age of the woody elements. The maximum area of small elements in a km grid (100 ha) was 19.9 ha; the maximum area of large elements was 93.7 ha. The maximum cohesion of small elements increased from 3.8 ha for a dispersal range \1 km, to 4,072 ha for species with a dispersal range of [25 km. The maximum cohesion of large elements varied from 16 to 24,000 ha. Synergy where small elements enhance large elements For nine species the response curve shows that probability of occurrence increased with increasing cohesion of large elements in the surroundings (CL), and this effect was enhanced if the cohesion of small elements in the surroundings (CS) was high. This is illustrated in Fig. 3a for the passerine bird Bullfinch (Pyrrhula pyrrhula). Conversely, the occurrence of these species did not increase with the cohesion of small elements if cohesion of large elements was low (Fig. 3b). Of these nine species, interaction between CL and CS was significant for the Bullfinch and the butterflies Checkered Skipper (Carterocephalus palaemon) and White Admiral (Limenitis camilla) (Table 2). For eight species there was still a significant positive effect of CL without CS in the model. For five species, CS had a significant negative effect without CL in the model. For the butterfly Large Skipper (Ochlodes faunus) and the passerine bird Tree Pipit (Anthus trivialis), CS had a positive significant effect if included in the regression model together with CL.
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123 Carterocephalus palaemon Vaccinium vitis-idaea Satyrium ilicis Ochlodes faunus Certhia brachydactyla Anthus trivialis Limenitis camilla Parus montanus
Cowberry
Ilex Hairstreak Large Skipper
Short-toed Treecreeper
Tree Pipit
White Admiral
Willow Tit
Athyrium filix-femina
Lady Fern
Vaccinium myrtillus Blechnum spicant Celastrina argiolus Aegithalos caudatus Parus palustris Turdus viscivorus Neozephyrus quercus Pararge aegeria
Bilberry
Hard Fern
Holly Blue
Long-tailed Tit
Marsh Tit
Mistle Thrush
Purple Hairstreak
Speckled Wood
Two-way
Aphantopus hyperantus
Geum urbanum
Wood Avens
Ringlet
s, c, r
Stellaria holostea
Greater Stitchwort
Adoxa moschatellina
Picus viridis
European Green Woodpecker
Anthocharis cardamines
Carduelis carduelis
European Goldfinch
Moschatel
Melampyrum pratense
Common Cow-wheat
Orange Tip
a, s, d, r, agr
Polygonia c-album
Comma
p, m, r, w
a, agr, r
uw, p, r
a, s, agr, r, c
c
m, d
a, m, r
uw, a, p, c, r
s, uw, agr, m, r
s, uw, p, m, r, w
a, s, l, p, d, r
a, s, p, m, r
a, m, r
uw, m, r, w
a, l, c, r
uw, d, r
Pteridium aquilinum
uw, a, d, r, agr
uw, a, s, r
a, p, w, r, m
s, uw, d, r, agr
uw, a, l, r, p
uw, d, r uw, agr, r, w
uw, a, agr, r
a, agr, d, r
uw, a, d, r
Habitat quality variables
Bracken Fern
Large enhances small
Pyrrhula pyrrhula
Checkered Skipper
Scientific name
Bullfinch
Small enhances large
Species name per response type
Table 2 Results of regression analysis of 40 plant, butterfly and bird species
12.0
6.4
3.2
25.1
?
0.4
6.8
15.6
25.0
8.7
17.6
12.4
19.2
13.9
5.0
16.2
10.6
2.8
8.0
2.6
26.3
20.3
8.2
9.7 3.7
17.9
19.5
16.3
Mean dev null model
13.7
6.5
3.3
25.3
0.7
0.8
7.7
17.7
25.5
11.1
20.2
13.8
21.7
15.1
6.4
18.7
11.4
2.9
9.2
3.4
30.4
25.6
8.3
13.1 5.8
22.1
24.1
18.9
Mean dev final model
ns
ns
ns
ns
??
ns
ns
???
ns
ns
ns
ns
ns
--
-
ns
ns
ns
ns
???
??
???
ns
?? ???
???
??
???
CL
??
ns
ns
ns
ns
ns
?
ns
??? ns
??
???
??
??
??
ns
?
ns
??
-
-
ns
ns
ns
ns
-
--
CS
??
ns
ns
ns
???
ns
ns
???
ns ns
ns
ns
?
ns
ns
ns
ns
ns
?
??
ns
???
ns
? ???
???
??
???
CL in CL ? CS
???
ns
ns
ns
?
ns
??
???
??? ns
??
???
???
ns
??
ns
??
ns
???
ns
ns
??
ns
ns ?
ns
ns
ns
CS in CL ? CS
??
ns
ns
ns
ns
ns
ns
ns
? ?
??
?
?
?
ns
ns
??
ns
ns
ns ns
ns
?
?
CL * CS
1112 Landscape Ecol (2009) 24:1105–1121
Emberiza citrinella
Pyronia tithonus Araschnia levana Maianthemum bifolium
Hedge Brown
Map Butterfly
May Lily
Hippolais icterina Vanessa atalanta
Icterine Warbler
Red Admiral
agr, c, m, r
agr, m
a, agr, m, r
s, uw, m, r
a, s, l, d, r
s, m, r, w
uw, a, c, d, r
a, c, r
l, r, p uw, d, r
uw, a, p, agr, r, w
a, r
Habitat quality variables
2.0
3.8
3.2
4.9
19.9
2.5
36.1
19.9
10.1 6.5
12.1
3.1
Mean dev null model
2.1
5.2
3.2
5.3
20.8
2.5
35.9
20.0
12.4 7.7
12.4
3.0
Mean dev final model
ns
--
ns
?
-
ns
ns
ns
ns ns
ns
ns
CL
ns
ns
ns
?
??
ns
ns
ns
? ???
ns
ns
CS
ns
---
ns
ns
ns
ns
ns
ns
ns ns
ns
ns
CL in CL ? CS
ns
ns
ns
ns
??
ns
ns
ns
ns ???
ns
ns
CS in CL ? CS
ns
ns
ns
ns
ns
ns
ns
ns
ns ns
ns
ns
CL * CS
Significance of effects (? = positive, - = negative): ??? = p \ 0.001; ?? = p \ 0.01; ? = p \ 0.05
Habitat quality variables: 1. Area of each soil type present (c = clay, s = sand, p = peat, l = loam, agr = plaggen soils, uw = no soil, urban area or water). 2. Area of soils with different humidity (w = wet, m = moist, d = dry). 3. r = (sub) region in the Pleistocene sandy soils of the Netherlands (north, west, south, east). 4. a = maximum forest age for each km grid (a)
Species are grouped by type of response curve of predicted occurrence probability versus CL and CS (see Statistical analysis in methods). Per species, the following are shown: significant habitat quality variables in the null model, mean deviance of null model and final model and effects of cohesion of large elements (CL), cohesion of small elements (CS) and interaction between spatial variables (CL * CS) in the final model. Nomenclature according to van der Meijden (2005) for plants, SOVON (2002) for birds and Bos et al. (2006) for butterflies
Falco tinnunculus
Common Kestrel
Both negative
Cuckoo
Cuculus canorus
Sitta europaea
Eurasian Nuthatch
No preference
Listera ovata Oriolus oriolus
Common Twayblade Eurasian Golden Oriole
Small only
Gonepteryx rhamni
Yellowhammer
Scientific name
Brimstone
Large only
Species name per response type
Table 2 continued
Landscape Ecol (2009) 24:1105–1121 1113
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+++++ = at maximum value CL
+++++ = at maximum value CS
a
= at mean value CS ooooo = at minimum value CS
1.0
a
= at mean value CL
ooooo = at minimum value CL
0.8
p (occurrence)
0.8
p (occurrence)
1.0
0.6
0.4
0.2
0.6
0.4
0.2
0.0 0.0
5.0
10.0
15.0
CS
0.0
CL
0.0
spatial cohesion large elements (km²)
b
ooooo = at minimum value CL
0.8
= at mean value CS 1.0
ooooo = at minimum value CS
0.8
p (occurrence)
p (occurrence)
0.03
+++++ = at maximum value CS
= at mean value CL 1.0
0.02
spatial cohesion small elements (km²)
+++++ = at maximum value CL
b
0.01
0.6
0.4
0.2
0.6
0.4
0.2
CS 0.0
0.50
1.00
1.50
spatial cohesion small elements (km²)
CL
0.0 0.00
0.04
0.08
0.12
0.16
spatial cohesion small elements (km²)
Fig. 3 The increase of the probability of occurrence of the Bullfinch (Pyrrhula pyrrhula) versus cohesion of large elements (CL) is enhanced by higher values of cohesion of small elements (CS) (a). Probability of occurrence does not increase with cohesion of small elements (CS) with low cohesion of large elements (b)
Fig. 4 The increase of probability of occurrence of the Greater Stitchwort (Stellaria holostea) versus cohesion of small elements (CS) is enhanced by higher values of cohesion of large elements (CL) (a). Probability of occurrence does not increase with cohesion of large elements (CL) with low cohesion of small elements (b)
Synergy where large elements enhance small elements
surroundings (CS), and this effect was enhanced if the cohesion of large elements in the surroundings (CL) was high. In Fig. 4a, this is illustrated for Greater Stitchwort (Stellaria holostea). Here too, conversely, occurrence did not increase with the cohesion of large elements if cohesion of small elements was low (Fig. 4b).
For 11 species, the response curve showed exactly the opposite effect: probability of occurrence increased with increasing cohesion of small elements in the
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Landscape Ecol (2009) 24:1105–1121 Table 3 Matrix of area requirements for a key population (area) and dispersal capacity (disp) of the studied species
1115 Area 150 km 2
n=6
n=4
n=3
–
Bullfinch
European Goldfinch
n=4
n=1
n=2
n=1
–
–
–
–
Disp 25 km
Of these 11 species, interaction between CL and CS was significant for the birds European Goldfinch (Carduelis carduelis) and European Green Woodpecker (Picus viridis), the plant species Greater Stitchwort and Wood Avens (Geum urbanum) and the butterflies Orange Tip (Anthocharis cardamines) and Ringlet (Aphantopus hyperantus) (Table 2). Without CL, the positive effect of CS was still significant for eight species. For European Green Woodpecker and Greater Stitchwort CL had a significant negative effect without CS in the model. For Bracken Fern (Pteridium aquilinum) and Wood Avens CL had a significant possitive effect if included in the regression model together with CS.
n=1
n=1
–
–
Two-way synergy For eight species, both types of response curve were found: their probability of occurrence increased with the cohesion of both large (CL) and small elements (CS), and also if both networks were combined. Of these eight species, interaction between CL and CS was significant only for the Speckled Wood butterfly (Pararge aegeria) (Table 2). The shrub species Bilberry (Vaccinium myrtillus) and the passerine bird Long-tailed Tit (Aegithalos caudatus) had only a significant positive effect from CS if added together with CL in the regression model. For Speckled Wood the positive effect of CL was only significant together with CS in the regression model.
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1116 Table 4 Matrix of response type (response) and habitat function (habitat) of the studied species
Landscape Ecol (2009) 24:1105–1121 Response
Small
Large
enhances large
enhances small
Two-way
Habitat Preference
White Admiral
for large
Chequered Skipper
n=16
Bullfinch
Preference
–
European Green
Speckled Wood
Woodpecker
Wood Avens
for small
Greater Stichwort
n=20
Orange Tip
–
Ringlet
Habitat function is presented as showing a preference for either large or small elements for reproduction, dispersal or foraging (see Appendix— Electronic supplementary material). Per matrix class the names are presented of those species showing synergy. Synergy is indicated by a significant interaction between the effects of cohesion of small and large elements (CL * CS) on occurrence probability
European Goldfinch No preference
–
–
Chequered Skipper
European Green
Speckled Wood
n=4
Foraging in ma trix
Woodpecker
n=18
Orange Tip
No synergy In the response curve of the Brimstone butterfly (Gonepteryx rhamni) and the passerine bird Yellowhammer (Emberiza citrinella), the occurrence probability increased with increasing cohesion of large elements in the surroundings (CL). This effect was inversely related to the cohesion of small elements in the surroundings (CS), but there were no significant effects of large elements or of interaction (Table 2). The response curves of six other species showed that occurrence probability increased with increasing cohesion of small elements (CS). This effect was inversely related to the cohesion of large elements in the surroundings (CL), but no significant interaction was found for these species (Table 2). For the orchid
123
–
Ringlet European Goldfinch
Common Twayblade (Listera ovata), the passerine bird Eurasian Golden Oriole (Oriolus oriolus) and the forest herb May Lily (Maianthemum bifolium) the positive effect of CS was significant. For May Lily, CL had a negative effect without CS in the regression model. The response curve of the parasitic bird Cuckoo (Cuculus canorus) showed that occurrence probability increased with cohesion of large elements (CL), but also with cohesion of small elements (CS). These variables are interchangeable: when both variables were added to the regression model, the effects were no longer significant. The response curves of the bird of prey Common Kestrel (Falco tinnunculus), the passerine bird Icterine Warbler (Hippolais icterina) and of the migratory butterfly Red Admiral (Vanessa atalanta) showed
Landscape Ecol (2009) 24:1105–1121
that occurrence probability decreased both with cohesion of large (CL) and with cohesion of small elements (CS). Only the Icterine Warbler (Hippolais icterina) had a (strong) negative effect from CL. Synergy and species traits concerning spatial scale Seven out of the 10 species that showed a significant interaction between large and small elements have a limited dispersal capacity (\1 or 1–3 km) and a limited area requirement for a key population (\1 km2). Given the fact that there are 14 species in this spatial trait (see also Appendix—Electronic supplementary material), this means that 50% of these species showed synergy significantly (Table 3). Two other species showing significant interaction indicating synergy were the European Goldfinch and the Bullfinch. These bird species are more mobile with a dispersal capacity of 3–15 km, and have much higher area demands: up to 10 or 150 km2 for a key population. They represent 18 percent of the eleven studied species having this spatial trait. Remarkably, 10 species with the same dispersal capacity but lower area demands do not show synergy. The other five studied species belonged to the highest dispersal classes (15–25 and [25 km) with area demands for a key population varying from l to [150 km. Of the species with this spatial trait only one (20%), the European Green Woodpecker, showed significant interaction. Synergy and habitat function We will now reconsider the 10 species showing a significant interaction between large and small elements, but now focusing on the habitat function of both networks (Table 4). It appears that five of these species have a preference for large elements for reproduction and use small elements for dispersal according to expert judgement (see also Appendix— Electronic supplementary material). This may correspond to the found type of response: White Admiral, Chequered Skipper and Bullfinch have an effect where small elements enhance large ones. However, the European Green Woodpecker showed an effect where large elements enhance small elements while Speckled Wood showed a two-way synergy. The other five species showing synergy significantly have an expected preference for small
1117
elements for reproduction and dispersal, but also use large elements for reproduction, except the European Goldfinch. This corresponds to the response type of these species that show an effect where large elements enhanced the effect of small elements. Of the 10 species showing synergy, six are bird and butterfly species that use the matrix, the open agricultural land surrounding small elements, for foraging. Four of these species showed an effect where large elements enhance small elements.
Discussion Of the 40 plants, butterflies and birds studied, 28 species showed a response curve that indicated synergy between large and small elements. Nine of these species seem to benefit mostly from large elements while small elements have an added value. This is indicated by two effects: cohesion of small elements has a significant positive effect only in conjunction with cohesion of large elements, or the cohesion of small elements has a negative effect when included as only spatial parameter illustrating the negative effect of the absence of large elements. Eleven other species benefit mostly from small elements while large elements have an added value. This is illustrated by the positive effect of cohesion of large elements found only in conjunction with cohesion of small elements, or by the negative effect of cohesion of large elements when included as only spatial parameter, indicating the negative effect of the absence of small elements. Eight species showed two-way synergy, i.e. show both effects. For these species, small elements enhance the positive effect of large elements, whereas large elements enhance the effect of small elements. Twelve Species showed no synergy. They occurred mostly in landscapes dominated by either large or small elements, or their response curve had no clear relation to small or large elements. Clearly not all species benefit from combining small and large scale networks. Despite being expected to prefer large elements (see Appendix—Electronic supplementary material), the Eurasian Golden Oriole and the Common Twayblade showed a positive effect from small elements. May lily had a preference for small elements, while no special preference was
123
1118
expected. A possible explanation for the interchange ability of small and large networks for the Cuckoo is that it not only parasitizes birds in woodland where it may prefer large elements, but also birds breeding in reeds or marshes, where it may prefer small elements. Synergy seems to be related to species traits concerning spatial scale, namely to a limited dispersal capacity and small area capacity (Table 3). The importance of dispersal capacity for synergy seems plausible, as species with low dispersal capacity are not able to disperse between large elements without using the intervening small elements. Second, species with small area requirements will be able to use both networks, thus the combination of small and large elements will render large networks. The different types of synergy seem to reflect the variation in habitat functions of the two networks (Table 4, see also Appendix—Electronic supplementary material). Species showing synergy where small elements enhance the positive effect of large elements often have a preference for large elements. For the White Admiral and Checkered Skipper this preference may reflect the need for high habitat quality like wet, calcareous or nutrient-poor conditions that are not found in small elements in intensively used landscapes. For species like the Bull Finch, preference for large elements is due to their large area requirements. The additive value of small elements for these species can probably be explained by its function as (suboptimal) reproduction and/or dispersal habitat. Species showing synergy where large elements enhance the positive effect of small elements often have a preference for small elements. This preference may reflect the use of the matrix for foraging by birds and butterflies like Ringlet, Orange Tip, European Goldfinch and European Green Woodpecker. Orange tip also uses cuckooflower in grasslands to lay its eggs. Plant species like Wood Avens and Greater Stichwort may prefer small elements because of the greater age of wooded banks compared to many woodlands, the better light conditions, and less accumulation of litter. The additive value of large elements for these species is probably that it offers suboptimal reproduction habitat or that edges or open spaces are used for reproduction habitat. Speckled Wood, which showed a strong two-way synergy, has subpopulations that prefer either large or small elements; the range of the species is shifting northwards in the Netherlands, using small elements
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Landscape Ecol (2009) 24:1105–1121
during migration (Merckx 2005; Thomas et al. 1994; Bos et al. 2006).
Conclusions and implications for landscape planning This analysis confirmed our hypothesis that the occurrence of the studied species could be explained better by a combination of spatial cohesion of nature areas (large elements) and green veining (small elements) in the matrix than by both networks separately. The synergistic effect of combining woodlands [5 ha with green veining was most pronounced for species that function on a small spatial scale, especially for species with a dispersal capacity lower than 3 km and an area requirement \1 km2. For species showing synergy, green veining often seems to have multiple functions. Based on our correlative study it is not possible to decide which function is most important. Small scale species potentially benefit most because both networks have a function as reproduction habitat. Thus by the combination of woods and green veining both networks contribute to spatial cohesion and enlarge the total network size. Second, species with limited dispersal capacity will benefit from the higher habitat density in combined networks. Third, the presence of green veining in the matrix will increase matrix permeability both for dispersing and for foraging behavior. We expect that the benefit for protected species will be even higher due to their more isolated presence in the Natura 2000 network. These insights can be applied to landscape planning by integrating the different policy instruments used for design and management of nature reserves and small elements in the agricultural matrix. At the moment, nature reserves like Natura 2000 sites are managed by the national government or nature organisations, while green veining is in hands of local governments or is privately owned. Due to different ownership and different policy instruments playing at different time and spatial scales, an integrated management of nature reserves and the surrounding landscapes will not take place automatically. In order to achieve this, landscape instruments of local governments and nature reserve instruments of national governments should be put together, where higher policy levels may set the preconditions
Landscape Ecol (2009) 24:1105–1121
for lower levels. In intensively used areas one should also search for new policy constructions where ecological goals of nature reserves and green veining can be combined with other goals like climate adaptation, recreation and water retention. A possible example on a local scale is to find new instruments to finance nature management by farmers or private owners in designated zones surrounding nature reserves to render ecological and recreational benefit. On higher policy levels, green veining can be used to create corridor zones between and climate adaptation zones around nature reserves. These zones should enable species of different habitat functions to find suitable combinations of habitats. This can be reached if nature reserves are surrounded by a combination of large and small elements, followed by areas with small elements only, and finally ending in more open agricultural and peri-urban landscapes. In this way, a coherent and multifunctional ecological network could become reality. Acknowledgements This research was financed by the program ‘‘Vernieuwend Ruimtegebruik’’ of Habiforum and cofinanced by the Dutch Ministry of Agriculture, Nature and Food Quality. We thank FLORON for providing presence/absence data from the national flora database FlorBase-2L (1975–2003). FlorBase is a database with plant species observations on a scale of 1 9 1 km. It is a compilation of data from Provinces, private partners, organisations of nature management and research institutes. We also thank Wies Akkermans and Paul Goedhart from BIOMETRIS for their support in statistical analyses and Joy Burrough for her advice on English writing.
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