Cozzi, G; Müller, C B; Krauss, J (2008). How do local habitat management and landscape structure at different spatial scales affect fritillary butterfly distribution on fragmented wetlands? Landscape Ecology, 23(3):269-283. Postprint available at: http://www.zora.uzh.ch University of Zurich Posted at the Zurich Open Repository and Archive, University of Zurich. Zurich Open Repository and Archive http://www.zora.uzh.ch Originally published at: Landscape Ecology 2008, 23(3):269-283.
Winterthurerstr. 190 CH-8057 Zurich http://www.zora.uzh.ch
Year: 2008
How do local habitat management and landscape structure at different spatial scales affect fritillary butterfly distribution on fragmented wetlands?
Cozzi, G; Müller, C B; Krauss, J
Cozzi, G; Müller, C B; Krauss, J (2008). How do local habitat management and landscape structure at different spatial scales affect fritillary butterfly distribution on fragmented wetlands? Landscape Ecology, 23(3):269-283. Postprint available at: http://www.zora.uzh.ch
Posted at the Zurich Open Repository and Archive, University of Zurich. http://www.zora.uzh.ch
Originally published at: Landscape Ecology 2008, 23(3):269-283. How do local habitat management and landscape structure at different spatial scales affect fritillary butterfly distribution on fragmented wetlands?
Abstract
Habitat fragmentation, patch quality and landscape structure are important predictors for species richness. However, conservation strategies targeting single species mainly focus on habitat patches and neglect possible effects of the surrounding landscape. This project assesses the impact of management, habitat fragmentation and landscape structure at different spatial scales on the distribution of three endangered butterfly species, Boloria selene, Boloria titania and Brenthis ino. We selected 36 study sites in the Swiss Alps differing in (1) the proportion of suitable habitat (i.e., wetlands); (2) the proportion of potential dispersal barriers (forest) in the surrounding landscape; (3) altitude; (4) habitat area and (5) management (mowing versus grazing). Three surveys per study site were conducted during the adult flight period to estimate occurrence and density of each species. For the best disperser B. selene the probability of occurrence was positively related to increasing proportion of wetland on a large spatial scale (radius: 4,000 m), for the medium disperser B. ino on an intermediate spatial scale (2,000 m) and for the poorest disperser B. titania on a small spatial scale (1,000 m). Nearby forest did not negatively affect butterfly species distribution but instead enhanced the probability of occurrence and the population density of B. titania. The fen-specialist B. selene had a higher probability of occurrence and higher population densities on grazed compared to mown fens. The altitude of the habitat patches affected the occurrence of the three species and increasing habitat area enhanced the probability of occurrence of B. selene and B. ino. We conclude that, the surrounding landscape is of relevance for species distribution, but management and habitat fragmentation are often more important. We suggest that butterfly conservation should not focus only on a patch scale, but also on a landscape scale, taking into account species-specific dispersal abilities. Landscape and management affect butterflies
1 How do local habitat management and landscape structure at different
2 spatial scales affect fritillary butterfly distribution on fragmented wetlands?
3
4 Gabriele Cozzi 1, Christine B. Müller 1 and Jochen Krauss 1,2
5
6 1Institute of Environmental Sciences, University of Zürich, Winterthurerstrasse 190, CH-8057
7 Zürich, Switzerland
8 2Department of Animal Ecology I, Population Ecology, University of Bayreuth,
9 Universitätsstrasse 30, D-95447 Bayreuth, Germany
10
11 Corresponding author: Jochen Krauss
12 E-mail: [email protected]; Tel. 0049-(0)921-55 26 48; Fax. 0049-(0)921-55
13 2784
14
15 Running title: Landscape and management affect butterflies
16 Date of manuscript draft: July 10th, 2007
17
18 Word count (total): 8243
1 Landscape and management affect butterflies
19 Abstract
20 Habitat fragmentation, patch quality and landscape structure are important predictors for
21 species richness. However, conservation strategies targeting single species mainly focus on
22 habitat patches and neglect possible effects of the surrounding landscape. This project assesses
23 the impact of management, habitat fragmentation and landscape structure at different spatial
24 scales on the distribution of three endangered butterfly species, Boloria selene, Boloria titania
25 and Brenthis ino. We selected 36 study sites in the Swiss Alps differing in (1) the proportion of
26 suitable habitat "wetland" and (2) the proportion of potential dispersal barriers "forest" in the
27 surrounding landscape, as well as in (3) altitude, (4) habitat area and (5) management (mowing
28 vs. grazing). Within the adult flight period three surveys per study site were conducted to
29 estimate occurrence and density of each species. The probability of occurrence was enhanced
30 by the increasing proportion of wetland for the best disperser B. selene on a large spatial scale
31 (radius: 4000 m), for the medium disperser B. ino on an intermediate spatial scale (2000 m)
32 and for the poorest disperser B. titania on a small spatial scale (1000 m). Nearby forest did not
33 negatively affect butterfly species distribution but instead enhanced the probability of
34 occurrence and the population density of B. titania. The fen-specialised B. selene had a higher
35 probability of occurrence and higher population densities on grazed compared to mown fens.
36 The altitude of the habitat patches affected the occurrence of the three species and increasing
37 habitat area enhanced the probability of occurrence of B. selene and B. ino. We conclude that,
38 apart from management and habitat fragmentation, the surrounding landscape might be
39 important for species distribution too. We suggest that butterfly conservation should not focus
40 only on a patch scale, but also on a landscape scale, taking into account species-specific
41 dispersal abilities.
42
43 Keywords: connectivity; detectability; dispersal; fens; habitat quality; landscape context;
44 metapopulations; population density; Switzerland
2 Landscape and management affect butterflies
45 Introduction
46 Fragmentation, habitat loss and deterioration of habitat quality are major threats to
47 biodiversity and increase the risk of species extinction (Debinski and Holt, 2000). As many
48 species persist as metapopulations in isolated, well defined habitat patches (Hanski, 1999) the
49 protection of these habitat patches is a main focus in conservation biology (e.g. Haight et al.
50 2002). In a metapopulation approach the patch matrix is considered as uniform and hostile and
51 it accounts only for patch connectivity, but other spatial effects are neglected (Hanski, 1999).
52 However, many ecological processes occur at spatial scales larger than the patch scale (Steffan-
53 Dewenter et al. 2002). Species distribution should therefore not only depend on habitat specific
54 characteristics such as habitat area and quality but also on the surrounding landscape at
55 different spatial scales, which goes beyond metapopulation connectivity measures.
56 Species-specific responses at different spatial landscape scales are little understood (but
57 see Roland and Taylor, 1997), but landscape effects on community responses have been
58 reported (e.g. Weibull et al. 2000; Atauri and De Lucio, 2001; Steffan-Dewenter et al. 2002;
59 Krauss et al. 2003a; Clough et al. 2005; Thies et al. 2005; Öckinger and Smith, 2006). One
60 predictor for single species distribution is the amount of available habitat in the surrounding
61 landscape (e.g. Heikkinen et al. 2004; Binzenhöfer et al. 2005), which is usually correlated
62 with isolation and connectivity measurements used in metapopulation studies (Winfree et al.
63 2005). A further landscape predictor, which might be important for species distribution is the
64 amount of potential dispersal barriers (van Dyck and Baguette 2005), but studies testing the
65 effects of dispersal barriers in multiple landscapes are lacking. Apart from the surrounding
66 landscape, habitat patch-specific characteristics are important for the colonisation and survival
67 of species (Hanski, 1999; Thomas, et al. 2001). Habitat area (Connor et al. 2000; Krauss et al.
68 2004, 2005), habitat quality (Thomas et al. 2001) and altitude (Boggs and Murphy, 1997;
69 Wettstein and Schmid, 1999) affect the distribution of species. Management strategies can be
70 applied to enhance habitat quality and can be actively controlled and adapted to match
3 Landscape and management affect butterflies
71 particular target species requirements (Pöyry et al. 2005, Johst et al. 2006). Management
72 therefore plays a key role in conservation practice. However, management concepts on
73 landscape scales are only starting to develop and require further landscape-scale field studies
74 (Moilanen et al. 2005).
75 Wetlands in central Europe harbour a high number of endangered and rare species and
76 are of high importance for species conservation (BUWAL, 2002). In the last decades,
77 Switzerland has seen a massive reduction of wetlands to only 10 % of their former areas
78 (BUWAL, 1990). A Swiss citizens' initiative in 1989 (Rothenturm Initiative) resulted in the
79 protection of the remaining wetlands to prevent further habitat and species loss. For the semi-
80 natural fens – a wetland type, which depends on regular management – late season mowing or
81 low impact cattle crazing are applied. Conservation strategies involving the broader
82 surrounding landscape do not exist, however agriculture and forestry occurring adjacent to fens
83 are restricted to low intensity management without fertiliser application (BUWAL, 2002).
84 Butterflies are assumed to represent adequate indicators of change for many terrestrial
85 insect groups (Thomas, 2005, but see Vessby et al. 2002) and often occur in metapopulations
86 (Hanski, 1999). In our study system, with patchily distributed wetlands, the three fritillary
87 butterfly species Boloria selene, Brenthis ino and Boloria titania are wetland specialists
88 throughout the northern Swiss Alps (Lepidoptera Specialist Group, 1991). We expect that these
89 three butterfly species perceive forest as a barrier, as it was reported that butterflies inhabiting
90 open habitats change their flight direction, when they encounter forest borders (Cant et al.
91 2005). We further assume that more mobile species react to the surrounding landscape at larger
92 spatial scales than more sedentary species, according to findings for parasitoid species (Roland
93 and Taylor 1997).
94 In this study we analyse the distribution of the three butterfly species B. selene, B.
95 titania and B. ino on 36 different patchy wetland habitats in different landscapes. We
96 investigated the effects of landscape structure, altitude, habitat area, and management on
4 Landscape and management affect butterflies
97 occurrence and density of the three species. In particular, we addressed the following
98 questions: (1) Does increased proportion of wetlands in the surrounding landscape increase the
99 probability of occurrence and population density? (2) Does increased proportion of forest in the
100 surrounding landscape (possible dispersal barrier) decrease the probability of occurrence and
101 population density? (3) Do species react to the landscape composition at different spatial scales
102 according to their dispersal ability? (4) Is grazing or mowing more suitable for the conservation
103 of the three wetland specialised fritillary butterflies? (5) How do habitat area and altitude affect
104 the distribution of the three butterfly species?
105
106 Materials and Methods
107 Study species
108 The Small Pearl-bordered Fritillary Boloria selene (Dennis and Schiffermüller), the
109 Titania's Fritillary Boloria titania (Esper) and the Lesser Marbled Fritillary Brenthis ino
110 (Rottemburg) are three butterfly species that typically inhabit wetlands in the montane and
111 subalpine region of the northern Swiss Alps (Lepidoptera Specialist Group, 1991). All three
112 species occur regularly on fens, but they show different degrees of specialisation. Boloria
113 selene is a characteristic fen inhabitant and its main larval food plants are Viola palustris and
114 V. canina. Boloria titania prefers tree-rich wetlands or wetlands with nearby forest and its
115 larvae feed on Polygonum bistorta and several Viola species. Brenthis ino prefers fens but may
116 also occur on extensively used meadows; its larvae feed on multiple Rosaceae plants species,
117 preferably Filipendula ulmaria but Sanguisorba officinalis and Potentilla palustris can also be
118 used (Ebert and Rennwald, 1991; Lepidoptera Specialist Group, 1991). All larval food plants
119 were common in our 36 study sites, but were not detected quantitatively in a parallel plant
120 community study due to their low visibility on fens during the butterfly survey period in June
121 to August (Peintinger, pers. comm.). The adults of all three species feed on a broad spectrum of
5 Landscape and management affect butterflies
122 flowering plant species, many of which are available on fens (Ebert and Rennwald, 1991;
123 Lepidoptera Specialist Group, 1991). The three fritillary species occur in Switzerland up to
124 about 2000 m a. s. l. but B. titania is not found below 800 m a. s. l. while the other two species
125 can occur at low altitudes. Boloria titania and B. ino are univoltine, whereas B. selene might
126 have a second generation at lower altitudes (Lepidoptera Specialists Group, 1991). In our study
127 sites all three butterfly species had only one generation. Finnish butterfly experts rank B. selene
128 and B. ino as better dispersers than B. titania in questionnaire evaluations (Komonen et al.
129 2004), and Dutch and German experts assume that B. selene is a better disperser than B. ino
130 (Bink, 1992; Weidemann, 1995). The average forewing length of adult male butterflies is
131 similar between the three species with 21 to 24 mm in B. titania, 18 to 21 mm in B.selene and
132 17 to 20 mm in B. ino (Higgins and Riley 1970). All three fritillary butterfly species are listed
133 as endangered species in the red data book of Switzerland (BUWAL, 1994).
134
135 Study region and study sites
136 The study region (3900 km2) is located in the Swiss Alps and Pre-Alps in the cantons of
137 St. Gallen, Schwyz, Glarus and Appenzell (Fig. 1). This landscape is dominated by arable land
138 and grassland (53.4 %) and by forest (28.0 %). Mountains with bare rocks (7.0 %), settlements
139 (5.5 %), lakes and rivers (4.8 %) and wetlands (1.3 %) cover smaller proportions of the region.
140 Several high mountains of up to 2500 m a. s. l. exist in the region. The 36 selected wetland
141 study sites are montane calcareous fen meadow communities of the Caricion davallianae
142 alliance (Wettstein and Schmid, 1999; Peintinger et al. 2003). The selection of these study sites
143 was stratified into two management practices (mowing vs. grazing) equally distributed along
144 an altitudinal gradient from 800 to 1400 m a. s. l. to reduce correlations between these two
145 predictor variables (Peintinger et al. 2003). Within the two management strategies and the
146 altitudinal gradient, study sites were randomly chosen out of the Swiss wetland inventory,
147 comprising more than 300 sites within the study region (BUWAL, 1990). Mean ± standard
6 Landscape and management affect butterflies
148 errors, minima and maxima of the patch characteristics are presented in Table 1. The five main
149 predictor variables are (1) proportion of wetland, (2) proportion of forest, (3) altitude, (4)
150 habitat area and (5) management.
151 Landscape analyses were conducted on the basis of a 1:25 000 landscape map, provided
152 by the Swiss Federal Office of Topography (Swisstopo) using the software “ArcGIS 9.0” from
153 the Environmental Systems Research Institute (ESRI). This map contains twenty-six “surface
154 area objects”, which we grouped into five categories (1) forests: tree nursery, debris in forest,
155 debris in open forest, swamp in forest, swamp in open forest, open forest, forest; (2) waters:
156 river, lake; (3) rocks: rock, debris on glacier, debris, glacier, gravel pit, clay pit, quarry; (4)
157 settlements: settlement, concrete dam, embankment dam; (5) others: shrubs, debris and shrubs,
158 grass tracks, dirt tracks, vines, swamp and shrubs, swamp, arable land. A sixth category was
159 added by us to the map; it contained all the wetlands (fens and bogs). The digital map of the
160 wetlands was provided by the Swiss Federal Institute for Forest, Snow and Landscape
161 Research (WSL). Landscape analyses were conducted for different nested spatial scales around
162 the centre of each study site (radii of 500 m, 1000 m, 2000 m, 3000 m, 4000 m). For each
163 surrounding landscape scale, proportion of wetland (surface area category 6), which is the
164 habitat of the three fritillary butterfly species, and proportion of forest (category 1), which is a
165 potential dispersal barrier, were chosen as predictor variables. Further potential dispersal
166 barriers like rocks (category 3), lakes (category 2) and settlements (category 4) have very low
167 area coverage within the landscapes studied, and did not substantially change the results when
168 added to the proportion of forest (results not shown). Therefore, only proportion of forest was
169 considered as a potential dispersal barrier for butterflies. The proportion of forest at the 500 m
170 scale was correlated with the proportion of forest adjacent to (surrounding) the fen border
171 (Spearman: N = 36; r = 0.63; p < 0.001). Similarly, the proportion of wetland (log10 + 1
172 transformed) at the 500 m scale was correlated with the distance to the next wetland (log10-
173 transformed) (Spearman: N = 36; r = –0.70; p < 0.001). In both cases the landscape predictors
7 Landscape and management affect butterflies
174 were chosen as main predictor variables. For calculations of proportion of wetland within a
175 landscape, the area of the study site was always subtracted from the landscape area to achieve
176 independence of the two predictor variables habitat area and proportion of wetland. Further
177 connectivity measurements were not tested, as they are inappropriate for very high proportions
178 of nearby habitat (see Fig. 1b) and they are generally correlated with each other (Krauss et al.
179 2003a; Winfree et al. 2005).
180 The altitude for each study site was achieved by calculating the mean altitude of each
181 butterfly transect, which was recorded with a GPS (Garmin eTrex Legend) and averaged for
182 each study site for the three butterfly surveys conducted. To calculate the habitat area with
183 Geographic Information Systems (GIS), patches within a distance smaller than 35 m between
184 the patch borders on the electronic map were assumed to be one patch. This distance was not
185 set a priori but resulted from a compromise between the imprecise digital maps on this scale
186 and the reality of patch borders in the field. Setting a critical distance at 50 m or 100 m instead
187 of 35 m resulted essentially in the same patch areas (Spearman: 0.958 ≤ r ≤ 0.990; all p-values
188 < 0.001), and in similar overall results (results not shown). Twenty of the chosen fens were
189 managed by late-season mowing (once a year after the 1st of September), while sixteen were
190 cattle-grazed. A fen was defined as grazed, if livestock or livestock tracks were detected at
191 least once during the site visits. Although the management history of previous years is
192 unknown, management changes within the last years are unlikely and changed only slightly
193 within the last 10 years (Wettstein and Schmid, 1999). The proportion of plants in flower per
194 study site was recorded during butterfly surveys by visual estimation of the area covered by
195 flowering plants along the butterfly transect. This proportion of plants in flower pooled for the
196 three surveys was significantly higher on mown compared to grazed fens (GLM: F1,35= 4.15; p
197 = 0.049).
198 None of the five main predictor variables (1) proportion of wetland, (2) proportion of
199 forest, (3) altitude, (4) habitat area and (5) management were significantly intercorrelated and
8 Landscape and management affect butterflies
200 all correlation coefficients were smaller than 0.3 (Table 2), reducing the common problem of
201 multicollinearity between predictor variables (Graham 2003).
202
203 Data collection
204 In summer 2005, all 36 study sites were surveyed for butterflies three times during the
205 periods 16 June to 28 June, 3 July to 20 July and 21 July to 9 August. The relatively small time
206 span of each survey period allowed minimal bias in butterfly distribution due to phenology
207 effects. The surveys within each period were arranged to minimise travel distance and to
208 maximise treatment combinations visited at one day. Within sites, random transects were
209 walked for 20 minutes in the 13 sites smaller than 3 ha, for 40 minutes in the 12 sites between
210 3 and 10 ha and for 60 minutes in the 11 sites larger than 10 ha; this achieved a similar habitat
211 area corrected probability of occurrence for each species (Krauss et al. 2003a). Butterflies were
212 recorded within a 5 m corridor, within which the detectability of the three similar species was
213 assumed to be constant. The speed of the recorder was ~ 2.5 km/h. The average length of each
214 transect per study site was 820 ± 240 m for the 20 minute counts, 1800 ± 540 m for the 40
215 minute counts and 2630 ± 730 m for the 60 min counts. On sites smaller than 3 ha all fritillary
216 butterflies counted were caught and released at the end of the survey to avoid repeated
217 counting of the same individuals. Double counts are much less likely to occur on large patches
218 where the proportion of sampled area is lower compared to small patches.
219 Occurrence data (presence vs. absence) was analysed for the 36 study sites, whereas population
220 densities were only considered for fens where a species occurred. Species were assumed to
221 inhabit a study site if at least one individual was encountered in one of the three surveys. The
222 length of each transect was measured with a portable GPS (Garmin eTrex Legend) and this
223 measure was subsequently used to estimate densities expressed as individuals/ha for each
224 species and study site. Population density of each species was summed up over the three
225 surveys. Butterfly surveys were conducted only in appropriate weather conditions that
9 Landscape and management affect butterflies
226 guaranteed butterfly activity (temperature: > 17°C, wind: < 3 Beaufort scale, no complete
227 cloud cover) and at appropriate times of the day (10.00 – 17.00) as proposed by Pollard (1977).
228 Temperature (Mean ± SE: 32 ± 1 °C, Min: 24 °C, Max: 36 °C) and estimated percent sunshine
229 (Mean ± SE: 82 ± 3 %, Min: 48 %, Max: 100 %) during surveys in our study did not correlate
230 with the population densities of the three butterfly species (all p > 0.33), which indicate that
231 butterfly observations were independent of climatic conditions under the defined monitoring
232 guidelines in our study.
233
234 Statistical analyses
235 The statistical analyses were performed using the software R 2.2.0 for Windows (R
236 Development Core Team 2004). The response variable "population density" of the three
237 butterfly species was log10 – transformed to meet the assumptions of normality and
238 homoscedasticity. To increase linearity of relationships, the predictor variable "habitat area"
239 was log10 – transformed and the predictor variable "proportion of wetland" was log10 + 1 –
240 transformed. Even though we surveyed 36 study sites, which is a relatively high replicate
241 number in landscape studies we have a low overall power to detect strong significances.
242 Therefore, we also consider marginally significant effects with p ≤ 0.1 as ecological
243 meaningful in this study. We further conducted hierarchical partitioning analyses, which
244 provide an estimate of independent contribution to the explained variance/deviance in multi-
245 factor models for each predictor variable separately and thus allow judging of the relative
246 importance of predictor variables independent of significances (Mac Nally and Walsh, 2004;
247 Heikkinen et al. 2005).
248 In a first step we conducted single factor tests for the five predictor variables
249 (proportion of wetland, proportion of forest, altitude, area, management) with the response
250 variables "butterfly occurrence" and "density". In a second step the predictors that showed at
10 Landscape and management affect butterflies
251 least marginal significances (p ≤ 0.1) in single factor analyses entered a multi-factor model
252 with sequential (Type 1 SS) and adjusted (Type 2 SS) sums of squares, using generalised linear
253 models (binomial) for occurrence data and general linear models for population density data
254 (Crawley 2002). We present adjusted sums of squares of multi-factor models, as sequential
255 sums of squares with predictors in the first position showed results very similar to the results
256 from single factor analyses. Like in other studies autocorrelations among nested radii are
257 frequent, and only the landscape scale showing the highest explanatory power for each of the
258 two predictors "proportion of wetland" and "proportion of forest" entered multi-factor models
259 (e.g. Steffan-Dewenter et al. 2002; Krauss et al. 2003a). In a third step the five predictor
260 variables, including one-way interactions and squared altitude were tested in multi-factor
261 models. As none of the interactions and squared predictors improved the models significantly,
262 and because of occasional problems with over fitted models with too many predictors we do
263 not further consider higher order predictors. Arithmetic means ± standard errors and ranges are
264 presented throughout the text and Table 1. Spearman correlations between the transformed
265 predictor variables are presented in Table 2.
266
267 Pre-analyses
268 In pre-analyses the spatial structure of occurrence and density data of the three butterfly
269 species was tested using Mantel test statistics based on Spearman's rank correlation with 1000
270 permutations and euclidian distances as similarity indices (Legendre and Legendre, 1998). The
271 occurrence of B. selene (r = 0.130, p = 0.018) and B. ino (r = 0.177; p = 0.032) and the density
272 of B. selene (r = 0.225; p = 0.023) were spatially structured, but not the occurrence of B. titania
273 (r = 0.013; p = 0.310) and densities of B. ino (r = 0.034; p = 0.293) and B. titania (r = 0.003; p
274 = 0.471). Where the response variables were spatially structured, the predictor variable
275 proportion of wetland was spatially structured too (all p ≤ 0.014). Therefore, the models
11 Landscape and management affect butterflies
276 indirectly account for the spatial structure of the response variables, as proportion of wetland is
277 a predictor. All other predictor variables were not spatially structured for the 36 study sites (all
278 p ≥ 0.13).
279 In further pre-analyses we estimated the detectability of the three study species
280 separately on our study sites using the free software Presence ver 2.0 (http://www.mbr-
281 pwrc.usgs.gov/software/doc/presence/presence.html) that implements the MacKenzie et al.
282 (2002) model. AIC model selection showed that the best model for all three species was one
283 with survey-specific detection probabilities. The estimated proportion of sites occupied did not
284 differ substantially from our naive estimate of occupied patches without correction for
285 detectability. Boloria selene had the lowest detectability (ranging from 20 % to 78 %,
286 depending on the survey). The analysis suggested that the species was detected at 94.9 % of the
287 occupied patches detected. For B. ino, detectability ranged from 68 % to 90 % and the species
288 was detected at 99.4 % of the occupied patches. For B. titania, detectability ranged from 61 %
289 to 95 % and the species was detected at 99.7 % of the occupied patches Therefore the
290 detectability of the three species on our study sites was similar and almost all occupied patches
291 were correctly identified as such. Thus, in our study imperfect detectability does not have to be
292 accounted for in the statistical analyses.
293
294 Results
295 A total of 1599 butterfly individuals of the three species Boloria selene (n = 233), B.
296 titania (n = 477) and Brenthis ino (n = 889) were recorded on the 36 wetland sites studied.
297 Boloria selene and B. titania occurred each on 23 sites and B. ino occurred on 32 sites.
298 Population densities (individuals/ha) of 12.85 ± 2.91 (range 0.76 to 67.90) for B. selene, of
299 30.50 ± 4.24 (range 2.04 to 109.30) for B. ino and of 25.35 ± 3.81 (range 3.05 to 71.84) for B.
300 titania were recorded, which are presented here as sums of the three surveys per study site.
12 Landscape and management affect butterflies
301
302 Occurrence
303 The occurrence of each of the three fritillary butterflies was predicted by the landscape
304 surrounding the study sites. The two landscape predictors, proportion of wetland and
305 proportion of forest, were tested at five different spatial scales (500 m, 1000 m, 2000 m, 3000
306 m, 4000 m). An increasing proportion of wetland positively affected the probability of
307 occurrence for all three fritillary butterfly species, but the spatial scale at which butterfly
308 occurrence was affected varied considerably among species (Table 3). The probability of
309 occurrence of B. selene was best predicted at a large spatial scale of 4000 m (Fig. 2 a, b). The
310 probability of occurrence of B. ino peaked at a medium scale of 2000 m (Fig. 2 c, d) and B.
311 titania at a small spatial scale of 1000 m (Fig. 2 e, f). The landscape predictor proportion of
312 forest did not significantly affect B. selene or B. ino occurrence at any spatial scale. However,
313 the probability of occurrence of B. titania increased with increasing proportion of forest at
314 almost all spatial scales, peaking at a scale of 500 m (Table 3, Fig. 2 g, h). Proportion of forest
315 adjacent to (surrounding) the fen borders was an even better predictor for B. titania occurrence
2 316 (N = 36; χ 1 = 13.31; p < 0.001; MF = 28.3 %).
317 The three patch-specific predictors (altitude, area, management) significantly affected
318 the occurrence of the three fritillary butterflies. The probabilities of occurrence of B. selene and
319 B. ino were enhanced by increasing habitat area, whereas B. titania was not significantly
320 affected by habitat area. Altitude significantly affected the occurrence of all three species with
321 probabilities of occurrence of B. selene and B. titania increasing, and the probability of
322 occurrence of B. ino decreasing with increasing altitude (Table 3). We recorded B. selene at all
323 sites above 1200 m a.s.l. but the probability to encounter B. ino sharply declined above 1200 m
324 a. s. l. to less than 40 % at 1400 m a.s.l. Boloria titania was found at all but one study site
325 above 1100 m a.s.l. The two different management regimes grazing and mowing affected only
326 the probability of occurrence of B. selene. Grazed fens had a significantly lower proportion of
13 Landscape and management affect butterflies
327 plants in flower (see methods), but a higher probability of occurrence of B. selene than did
328 mown fens (Table 3, Fig. 3 a). Brenthis ino and B. titania were not significantly affected by the
329 two different management strategies.
330
331 Population Density
332 The density of the three butterfly species was affected differently by the two landscape
333 predictors proportion of wetland and proportion of forest. An increasing proportion of wetland
334 at a large spatial scale of 3000 m affected the density of B. selene negatively (Fig. 4 a, b),
335 whereas B. ino and B. titania were not significantly affected by proportion of wetland at any
336 spatial scale (Table 3). Similar to the occurrence pattern, an increasing proportion of forest
337 enhanced the density of B. titania at the smallest scale of 500 m (Fig. 4 c, d), whereas no
338 significant effects of proportion of forest on densities of B. selene and B. ino were found (Table
339 3). Neither habitat area nor altitude affected the density of any of the three butterfly species
340 significantly. The density of B. selene was higher on grazed fens compared to mown fens (Fig.
341 3b), whereas the two different management strategies showed no significant effect on the
342 densities of B. ino and B. titania (Table 3).
343
344 Multifactor models
345 Single factor and sequential multi-factor models (Type 1 SS) with predictors in the first
346 position showed very similar results. Adjusted sums of squares (Type 2 SS) multi-factor
347 models showed more differences and some predictors even became non-significant when
348 corrected for all other significant predictor variables (Table 3). The hierarchical partitioning
349 models, which are independent from significant levels, reveal the relative importance of all
350 predictor variables in full multi-factor models. The independent effects of the two landscape
351 predictors in the occurrence models explain together 17.4 % for B. selene, 25.0 % for B.ino and
14 Landscape and management affect butterflies
352 28.9 % for B. titania, and in the population density models they explain 50.3 % for B. selene,
353 64.7 % for B.ino and 83.1 % for B. titania (Fig. 5).
354
355 Discussion
356 Our results showed that the two landscape factors, proportion of wetland and proportion
357 of forest in the surrounding landscape, affected fritillary butterfly occurrence and density at
358 different spatial scales. Hereby the two landscape predictors together explained in occurrence
359 models in average 24 %, in population density models 66 % of the variance. Habitat patch
360 specific factors, such as altitude, habitat area and management were also important for the
361 butterfly distribution of single species and explained in average the remaining 76 % in
362 occurrence models and 34 % in the population density models.
363 In agreement with other studies, the presence of additional habitat patches in the
364 surrounding landscape increased the probability of occurrence of butterfly species (Wettstein
365 and Schmid, 1999; Binzenhöfer et al. 2005). However, in our study the three butterfly species
366 reacted to the surrounding landscape at different spatial landscape scales indicating species-
367 specific differences in dispersal ability. Experts rank B. selene and B. ino as better dispersers
368 than B. titania, and B. selene as a better disperser than B. ino (Bink, 1992; Weidemann, 1995;
369 Komonen et al. 2004). This is consistent with our results, where the best disperser B. selene
370 reacted at the largest spatial scale, the medium disperser B. ino at a medium scale and the
371 poorest disperser B. titania at a small spatial scale. As the effects of proportion of wetland on
372 species distribution is at the significance level for all three species, further studies are
373 necessary to proof this scale dependency. However, scale-dependent effects of the landscape
374 structure have also been shown for whole insect communities (Weibull et al. 2000; Steffan-
375 Dewenter et al. 2002; Krauss et al. 2003a; Thies et al. 2005; Clough et al. 2005). In some
376 studies the scale dependence of species groups was even linked with the dispersal ability of
15 Landscape and management affect butterflies
377 these species groups, assuming that better dispersers react at larger spatial scales (Steffan-
378 Dewenter et al. 2002; Thies et al. 2005). Scale dependence for single species is less well
379 known but was linked to estimated species specific dispersal ability for parasitoids (Roland and
380 Taylor, 1997). As species with good dispersal ability should be able to cope better with poor
381 landscape connectivity than poor dispersers (Kareiva and Wennergren, 1995), the species B.
382 titania, the poorest disperser in our study, is likely to be more vulnerable to poor landscape
383 connectivity than the other two species we studied. Conservation strategies that aim to increase
384 patch connectivity would have to focus particularly on such poor dispersers.
385 In contrast to the enhanced probability of occurrence of B. selene with increasing
386 proportion of wetland, the density of B. selene decreased with increasing proportion of
387 wetland. Studies presenting density-area relationships for insects show positive, negative, non-
388 linear or non-significant relationships (Connor et al. 2000; Debinski and Holt, 2000; Hambäck
389 and Englund, 2005), with population densities of species being variable through time and being
390 dependent on landscape and species specific factors (Matter, 2003). Apart from stochastic
391 reasons, two explanations for increasing population densities with decreasing proportion of
392 habitats are plausible. One possibility is crowding due to habitat loss in the past forcing
393 individuals to move to the (few) remaining habitat patches (Debinski and Holt, 2000).
394 However with no relevant loss of wetlands in our study region within the last twenty years
395 (BUWAL, 2002), crowding is less probable. A second explanation is that butterflies tend to
396 have negative density-area relationships because of their largely visual searching behaviour
397 resulting in perimeter-related instead of area-related migration rates (Hambäck and Englund,
398 2005).
399 Apart from landscape analyses focusing on habitats in the surrounding landscape we
400 assumed that forests might act as dispersal barriers. This assumption is supported by several
401 studies, which show that butterflies specialised in open habitats perceive forest as a dispersal
402 barrier (Matter et al. 2004; Cant et al. 2005). For our study species we found no such evidence
16 Landscape and management affect butterflies
403 for forest to act as a dispersal barrier on any spatial scale, starting from a patch scale (adjacent
404 forest surrounding the fen border) to a landscape scale (proportion of forest) between 500 m –
405 4000 m radius. A possible explanation might be that individuals experience high forest
406 coverage – about 1/3 of the study region was covered by forests – not as a barrier, but cross it
407 in search of other suitable habitat patches. Whether individuals of the three fritillary species
408 cross the forest following corridors (Haddad et al. 2003) or whether they fly over or around the
409 forest is not known. Instead of representing barriers to dispersal that could limit butterfly
410 occurrence, nearby forests even enhanced the probability of occurrence and population density
411 of B. titania. This is in line with the habitat requirement of B. titania in Finland (Paukkunen et
412 al. 1999) and Switzerland, where B. titania is described as a specialist of tree-rich wetlands
413 (Lepidoptera Specialists Group, 1991). For this species forest borders are an important habitat
414 requirement as demonstrated by new observations, which describe that B. titania females lay
415 their eggs only in close proximity to forest borders, especially close to the spruce trees Picea
416 abies (Ebert, 2005). This emphasises the need to consider the distribution of species also at the
417 patch border and in the patch surrounding landscape and not only within strictly-defined
418 habitat patches. It is also assumed that some butterfly species have further requirements outside
419 edge-defined habitat sites like shelter and roosting places (Dennis et al. 2003). That habitat
420 patch size did not significantly affect the probability of occurrence of B. titania in our study
421 might be caused by the inappropriate definition of habitat and consequently of habitat size of
422 this species. Habitat patch size is usually a good predictor for the occurrence of butterfly
423 species showing an increased probability of occurrence with increasing habitat patch size
424 (Krauss et al. 2004, 2005), like it was found in our study for B. selene and B. ino.
425 Another important habitat characteristic for species distribution is the quality of a
426 habitat patch (Thomas et al. 2001), which can be improved by species specific management
427 strategies (Johst et al. 2006). One aspect of habitat quality is the proportion of plants in flower,
428 which act as the main food resource for adult butterflies (Ebert and Rennwald, 1991). Case
17 Landscape and management affect butterflies
429 studies could show that increasing proportion of plants in flower increase butterfly densities
430 and the probability of occurrence (e.g. Quinn et al. 1998; Krauss et al. 2003a). Our results
431 show that although grazed fens had a significantly lower coverage of plants in flower than
432 mown fens, none of the three butterfly species showed a reduced probability of occurrence or
433 lower densities on grazed fens. Instead, B. selene showed a higher probability of occurrence
434 and higher densities on grazed fens than on mown fens. As the management strategy could not
435 be linked to the presence of larval food plants (Peintinger, pers. comm.), the positive
436 relationship between B. selene and grazing is difficult to explain. Mowing has been shown to
437 negatively affect mean leaf area and the length of the leafstalk of Viola palustris (Jensen and
438 Meyer, 2001), the main larval food plant of B. selene. As larvae mostly feed on leaves and
439 stems, mowing may negatively affect the life cycle of B. selene, whereas grazing might be less
440 destructive for V. palustris and consequently B. selene larvae. However, both management
441 strategies are important for wetland conservation because they prevent plant succession into
442 forests, and are suitable to protect wetland-specialised species (Wettstein and Schmid, 1999).
443 Apart from management strategies, we have shown the importance to consider species
444 distribution along an altitudinal gradient, since altitude generally affects species distribution
445 (Wettstein and Schmid, 1999; Konvicka et al. 2003). The three butterfly species studied
446 showed different distribution patterns at different altitudes. Boloria selene and B. titania had a
447 higher probability of occurrence, and B. ino a lower probability of occurrence at higher
448 altitudes. If all three fritillary butterfly species were to be preserved simultaneously, there is a
449 need to protect wetlands both on the highlands and in the lowland regions, where human
450 activities constantly and quickly modify the territory. In the future, high altitude habitats might
451 become even more important if species distributions shift in response to global warming
452 (Konvicka et al. 2003).
453
454 Implications for conservation
18 Landscape and management affect butterflies
455 Our results demonstrate that not only patch specific habitat characteristics, but also the
456 surrounding landscapes can affect the distribution of endangered butterfly species. As the
457 butterflies consistently reacted at spatial scales larger than a single habitat patch and as some
458 species requirements may be met outside strictly defined habitat patches, conservation policies
459 for butterfly species should consider whole landscapes with connected patches, which allow
460 dispersal between habitat patches. In such landscapes, a minimum proportion of habitat should
461 be guaranteed that depends on the degree of mobility of each species. General habitat
462 management recommendations for all species are difficult, but for the target species B. selene
463 grazing of fens is more appropriate than mowing. The current politics in Switzerland, which
464 supports both management strategies, seems an appropriate solution as more diverse strategies
465 are better than uniformity (Schmid, 1996).
466
467 Acknowledgements
468 We thank, Stefan Birrer, Yann Clough, Alexander Fergus, Markus Peintinger, Matthias
469 Plattner, Jens Roland and Ingolf Steffan-Dewenter for constructive comments on the
470 manuscript and Ulrich Reyer for discussions, Bernhard Schmid and Benedikt Schmidt for
471 statistical advice and Janine Bolliger, Jasmin Joshi and the "Biodiversitätsmonitoring Schweiz
472 des Bundesamtes für Umwelt BAFU" for logistic help. Financial support came from the Swiss
473 National Science Foundation (grant no. 631-065950) and the Institute of Environmental
474 Sciences, University of Zürich. All experiments comply with the current laws of Switzerland.
19 Landscape and management affect butterflies
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25 Landscape and management affect butterflies
605 Table 1: Mean ± SE Minima and Maxima of the characteristics of the 36 fen habitats
606 investigated in this study.
607
Mean S.E. Min. Max.
Wetland (500m scale) in % 4.7 1.2 0.0 27.1 Wetland (4000m scale) in % 4.8 0.7 0.3 19.9 Distance to next wetland in m 420 80 35 - 100 2560 Forest (500m scale) in % 46.3 2.7 1.1 76.2 Forest (4000m scale) in % 37.1 1.3 21.0 52.6 Adjacent (surrounding) forest in % 57.5 3.6 0 100 Altitude in m a.s.l. 1090 30 800 1410 Habitat area in ha 7.5 1.0 0.9 22.3 Plants in flower in % (grazed) 17.9 2.2 7 43 Plants in flower in % (mown) 31.6 3.6 7 68
608
26 Landscape and management affect butterflies
609 Table 2: Spearman’s correlation coefficients (r) among transformed predictor variables of the
610 36 study sites (* p < 0.05; (*) p < 0.01; ns = not significant).
611
Wetland Forest Forest Altitude Habitat Management
(4000 m) (500 m) (4000 m) area
Wetland (500 m) 0.349 * -0.231 ns 0.055 ns 0.147 ns 0.069 ns -0.094 ns Wetland (4000 m) 0.027 ns 0.143 ns 0.123 ns 0.240 ns 0.011 ns Forest (500 m) 0.226 ns 0.265 ns 0.013 ns 0.059 ns Forest (4000 m) 0.020 ns 0.307 (*) 0.307 (*) Altitude 0.065 ns 0.253 ns Habitat area 0.199 ns
612
27 Landscape and management affect butterflies
613 Table 3: Generalised and general linear models on significant habitat and landscape predictors
614 for occurrence and density of the three butterfly species Boloria selene Boloria titania and
615 Brenthis ino. Test-statistics (χ2 or F) with degrees of freedom and significances (p) for single
616 factor analyses and multi-factor models with adjusted Sums of Squares (see Material and
617 Methods). MF = the percentage of deviance (occurrence) or the percentage of the variance
618 (density) explained by each predictor separately and MM = the percentage of deviance
619 (occurrence) or the percentage of variance (density) explained by the model in total.
620
Single factor analyses Multi-factor analyses
Test-statistic p MF % Test-statistic p MM %
Occurrence
B. selene (N = 36)
2 2 Wetland (4000 m) χ 1 = 4.07 0.044 8.6 χ 1 = 2.09 0.148 - 2 2 Altitude χ 1 = 6.76 0.009 14.4 χ 1 = 3.33 0.068 - 2 2 Habitat area χ 1 = 3.39 0.066 7.2 χ 1 = 2.79 0.095 - 2 2 Management χ 1 = 12.69 < 0.001 26.9 χ 1 = 10.84 < 0.001 - Model 49.1
B. ino (N = 36)
2 2 Wetland (2000 m) χ 1 = 3.60 0.058 14.3 χ 1 = 2.79 0.095 - 2 2 Altitude χ 1 = 7.43 0.006 29.6 χ 1 = 11.84 < 0.001 - 2 2 Habitat area χ 1 = 5.82 0.016 23.3 χ 1 = 6.52 0.011 - Model 80.0
B. titania (N = 36)
2 2 Wetland (1000 m) χ 1 = 3.34 0.067 7.1 χ 1 = 1.73 0.188 - 2 2 Forest (500 m) χ 1 = 5.90 0.015 12.5 χ 1 = 3.26 0.071 - 2 2 Altitude χ 1 = 22.18 < 0.001 47.1 χ 1 = 13.40 < 0.001 - Model 54.9
Density
B. selene (N = 23)
Wetland (3000 m) F121= 5.84 0.025 21.8 F120= 3.88 0.062 - Management F121= 4.88 0.038 18.9 F120= 2.20 0.153 - Model 30.1
B. ino (N = 32)
Forest (500 m) F130= 2.87 0.101 8.7 - - -
B. titania (N = 23)
Forest (500 m) F121= 5.93 0.024 22.0 - - -
28 Landscape and management affect butterflies
621 Fig. 1: a) The study region is located in the north-eastern part of Switzerland (small map; top
622 left) in the cantons St. Gallen Appenzell Glarus and Schwyz. The location of each of the 36
623 fens is shown by white dots. b) Example of one study site (shown by the arrow) with a high
624 proportion of wetland in the surrounding landscape. c) Example of one study site (arrow) with
625 a low proportion of wetland in the surrounding landscape. Radii of 500 m 1000 m and 2000 m
626 that were used in the landscape analyses are shown by circles in b) and c).
627 Black = wetlands; Grey = forests; Pale Grey = other habitats; White outlined = Lakes.
628
629 Fig. 2: Butterfly occurrence is affected by the surrounding landscape at different spatial scales
630 between 500 m and 4000 m. Nagelkerke R2 of simple logistic regressions for different spatial
631 scales are shown for proportion of wetland (a c e) and proportion of forest (g). B. selene
632 reacted strongest at a large spatial scale B. ino at a medium spatial scale and B. titania at a
633 small spatial scale. Simple logistic regression plots are shown for the spatial scale showing the
634 highest R2 values (b d f h).
635 * p-values ≤ 0.05; (*) p-values ≤ 0.1.
636
637 Fig. 3: Mean (± SE) for (a) the probability of occurrence and (b) population density of the
638 butterfly species B. selene predicted by management (grazed versus mown).
639
640 Fig. 4: Butterfly density is affected by the surrounding landscape at different spatial scales
641 between 500 m and 4000 m. R2 for simple regressions for different spatial scales are shown for
642 proportion of wetland (a) and proportion of forest (c). Simple linear regression plots are shown
643 for the spatial scale showing the highest R2 value (b d).
644 * p-values ≤ 0.05; (*) p-values ≤ 0.1.
645
29 Landscape and management affect butterflies
646 Fig. 5: Relative importance of the five predictor variables (independent effects) estimated from
647 hierarchical partitioning models for occurrence and population density of the three butterfly
648 species Boloria selene Boloria titania and Brenthis ino. The explained variance shows the
649 relative independent contribution of each predictor to the variance/deviance explained by the
650 multi-factor model with all predictors.
30 Landscape and management affect butterflies
651 Fig. 1:
a)
St. Gallen
Appenzell
Glarus Schwyz
5km 10km 20km
b) c)
652
31 Landscape and management affect butterflies 653 Fig. 2: a) Boloria selene b) Boloria selene scale 4000 m
0.25 100
0.2 80
0.15 * 60
0.1 40
Nagelkerke R-squared Nagelkerke 0.05 20 Probability of occurrence in % 0 0 0500 1000 2000 3000 4000 0 1 3 5 10 30
c) Brenthis ino d) Brenthis ino scale 2000 m
0.25 100 (*) 0.2 (*) 80 0.15 60
0.1 40
Nagelkerke R-squared Nagelkerke 0.05 20 Probability of occurrence in % 0 0 0500 1000 2000 3000 4000 0 1 3 5 10 30
e) Boloria titania f) Boloria titania scale 1000 m
0.25 100
0.2 80
0.15 (*) 60 0.1 40
Nagelkerke R-squared Nagelkerke 0.05 20 Probability of occurrence in % in of occurrence Probability 0 0 0 500 1000 2000 3000 4000 01351030
Landscape scale (m) Percentage of wetlands
g) Boloria titania h) Boloria titania scale 500 m
0.25 100 * 0.2 * * 80 * 0.15 60
0.1 40
Nagelkerke R-squared Nagelkerke 0.05 20 Probability of occurrence in % 0 0 0500 1000 2000 3000 4000 020406080
Landscape scale (m) Percentage of forest 32 Landscape and management affect butterflies
654 Fig. 3:
655
a) Boloria selene b) Boloria selene
100 30
80 20
60 10 8 40 5 20 3 Population in Ind./ha density Population Probability of occurrence in % 0 2 Grazed Mown Grazed Mown
Management Management 656
33 Landscape and management affect butterflies
657 Fig. 4:
658
a) Boloria selene b) Boloria selene scale: 3000 m
0.25 * 200 * 100 0.2 80 50 30 0.15 (*) 20 10
R-squared 0.1 8 5 3 0.05 2
PopulationinInd./ha density 1 0 0.8 0 500 1000 2000 3000 4000 01351030
Landscape scale (m) Percentage of wetlands
c) Boloria titania d) Boloria titania scale 500 m
0.25 * 200 100 0.2 80 50 30 0.15 20 10 8 R-squared 0.1 5 3 0.05 2
Population density in Ind./ha density in Population 1 0 0.8 0500 1000 2000 3000 4000 0 20406080
Landscape scale (m) Percentage of forest
659
34 Landscape and management affect butterflies
660 Fig. 5: 661 Occurrence Population density a) Boloria selene b) Boloria selene 0 50 Explained variance (%) variance Explained Explained variance (%) 0102030405 010203040 Wetland Forest Altitude Habitat Management Wetland Forest Altitude Habitat Management (4000 m) (3000 m) area (3000 m) (500 m) area
c) Brenthis ino d) Brenthis ino 0
40 Explained variance (%) Explained variance (%) 0102030405 0102030 Wetland Forest Altitude Habitat Management Wetland Forest Altitude Habitat Management (2000 m) (500 m) area (4000 m) (500 m) area
e) Boloria titania f) Boloria titania 70 70 60 60 50 50 Explained variance (%) variance Explained Explained variance (%) variance Explained 010203040 010203040 Wetland Forest Altitude Habitat Management Wetland Forest Altitude Habitat Management (1000 m) (500 m) area (500 m) (500 m) area
662
663
35