Herbivory on an Island Population of Chequered Blue Butterfly (Scolitantides Orion)

Direct and indirect effects of roe deer (Capreolus capreolus) herbivory on an island population of Chequered blue butterfly (Scolitantides orion)

Alexandra Johansson Degree project in biology, Master of Science (2 years), 2021 Examensarbete i biologi 30 hp till masterexamen, 2021 Biology Education Centre, Uppsala University, and The County Administrative Board of Stockholm Supervisors: Anssi Laurila and Miguel Jaramillo

Chequered blue (Scolitantides orion). All pictures and illustrations in this thesis have been taken/made by A. Johansson. Acknowledgement I want to thank everyone who has contributed, supported and helped me with this study. A special thanks to my two supervisors: Anssi Laurila and Miguel Jaramillo, who have provided me with a lot of helpful suggestions and feedback throughout the study. Anssi, for helping me dealing with all the (nasty) statistics and giving me feedback on the report. Miguel, for providing me with the opportunity to do this work for the County Administrative Board of Stockholm and guiding me in the field and giving me feedback how to improve my work. I also want to thank Göran Arnquist for helping me with some of the statistical parts, I would have never figured out the partly nested mixed-model ANOVA on my own... Also, a thank you to Julian Bauer for being my opponent and giving feedback on the report.

I also want to thank Sören Nissilä for lending me the cabin on Mörtö island and all the nice people on the island who walked by and talked to me during my lonesome days in the field. Finally, I want to thank my family and friends for the support during this challenging period, and special thanks to Jacob Ljungbäck for reading the report and giving me feedback.

Abstract Herbivory by ungulates is a known agent of disturbance in many ecological systems around the globe. At high abundances and through a selective foraging behaviour herbivory may inflict significant direct and indirect effects on local plant and animal communities. Direct effects refer to ungulates directly interacting with another species by foraging on plants and incidentally on insect eggs, larvae and/or adults. Indirect effects in this context describes ungulate feeding on plants which indirectly alters the availability and quality of food resources utilized by local insect fauna. Ungulates have even been considered as a potential conservation threat to persistence of some insect species. This aim of this thesis was to study the interaction between a common ungulate, the roe deer (Capreolus capreolus), and the rare butterfly Scolitantides orion by sharing the same plant resource, orpine (Hylotelephium telephium).

S. orion is a butterfly species of high conservation concern in Sweden. It is classified as endangered (EN) and has over the last decades experienced significant decrease in population size, mainly as a result of habitat loss and fragmentation. Roe deer has been considered as a potential threat to the population, but the magnitude of the threat has not been studied in detail. The objective of this thesis was to analyse the direct and indirect consequences of roe deer herbivory on S. orion population in the island of Mörtö, Stockholm archipelago. I predicted that roe deer alters the abundance of suitable host plants for oviposition and consumes egg and/or larvae, producing significant differences between protected and unprotected plants. The study was conducted in May-June 2020 in seven selected sites in Mörtö. At the onset of the butterfly season, 47 mesh cages were established sheltering 10.4% of the 1310 plants included in the study. Five inventories were conducted over a five-week time period, where each plant was examined by measuring the height, number of leaves, plant damage and the number of eggs and larvae. These data were used to examine the direct and indirect effect of roe deer foraging.

The results of this study could not confirm that roe deer has a significant direct or indirect effect on S. orion population in Mörtö. Hence, roe deer may not pose as large of a threat as initially expected, at least in this island at this time period. However, this was not the only result from this study. Plant properties such as leaf number and plant height (although not significant) as well as plant quality influenced the host choice for oviposition, with significantly more eggs being found on plants with more leaves and less plant damage. Hence, it seems that roe deer and other herbivores indirectly affects the host choice of S. orion by altering plant attractiveness. The reason for this behaviour needs to be studied in detail, but a proposed reason was that females choose plants with less damage to avoid competition and/or predation. Another finding was that a substantial number of eggs were lost over the season, some of which due to incidental feeding by roe deer and other herbivores. However, many eggs were lost without an identified reason, which likely is a result of disease or predators, but this needs to be studied further. Finally, the cage experiment was successful in keeping the roe deer out but not able to cause a difference in egg survival between protected and unprotected plants. This was likely due to low roe deer herbivory in general.

Even though the effect of roe deer on S. orion population was not as significant as expected, it is still important to consider roe deer as a potential threat to the species at its most vulnerable stages as the species is dependent on the host plant for its survival. Hence, it would be interesting to do a similar experiment in a locality with higher deer density to further investigate the effect of roe deer on S. orion populations.

Content ACKNOWLEDGEMENT ______1 ABSTRACT ______2 BACKGROUND ______5

DIRECT AND INDIRECT EFFECTS OF UNGULATE BROWSING ______5 Ungulate-Plant interactions ______5 Ungulate-Insect interactions ______6 Scolitantides orion ______8 OBJECTIVE ______8 Indirect effects ______8 Direct effects ______8 Protected vs. unprotected plants ______9 HYPOTHESIS ______9 MATERIAL & METHODS ______9

STUDY SPECIES ______9 STUDY SITE ______10 EXPERIMENTAL SET-UP ______11 Orpine inventories ______11 Deer exclusion cages ______11 DATA COLLECTION ______13 1. Estimating indirect effects of deer and other damage ______13 2. Estimating direct effects of deer damage ______14 3. Protected plants vs. non-protected (control) plants ______15 ANALYSES ______16

DATA PREPARATION ______16 1. HOST PREFERENCE ______16 1.1 Host preference: Egg Y/N vs. plant morphological traits ______16 1.2 Host preference: Egg max vs. plant morphological traits ______17 2. INDIRECT EFFECTS ______17 2.1 S. orion egg-laying behaviour: does plant damage (Y/N) have an effect? ______17 2.2 S. orion egg-laying behaviour: does plant damage (percent) have an effect? ______17 4. CAGED VS. UNCAGED PLANTS ______17 4.1 Comparison between control vs. caged plants: egg total______18 4.2 Comparison between control vs. caged plants: egg survival ______18 4.3 Comparison between control vs. caged plants: damage percent ______18 RESULTS ______19

1. HOST PREFERENCE ______21 1.1 S. orion host selection based on plant morphology, egg(Y/N) ______21 1.2 S. orion host selection based on plant morphology, Egg max ______21 2. INDIRECT EFFECTS ______22 2.1 S. orion egg-laying behaviour: does plants damage (Y/N) have an effect? ______22 2.2 S. orion egg-laying behaviour: does plants damage (in percent) have an effect? ______23 2.3. S. orion egg-laying behaviour: is plant quality affecting oviposition behaviour? ______24 3. DIRECT EFFECTS ______25 4. CONTROL (UNCAGED) VS. EXPERIMENTALLY PROTECTED (CAGED) PLANTS ______25 4.1 Comparison between control vs. caged plants: egg total______26 4.2 Comparison between control vs. caged plants: egg survival ______27 4.3 Comparison between control vs. caged plants: damage percent ______27 DISCUSSION ______28

INDIRECT EFFECTS ______29 DIRECT EFFECTS ______30 CAGE EXPERIMENT ______31 S. orion larvae ______31 Error sources ______31 CONCLUSION ______32 REFERENCES ______33

Background Herbivores are an important component of the ecosystem structure, function and dynamics. By interacting with biotic and abiotic environment herbivores shape communities and can drive ecosystem change over time (Naiman 1988). Wild ungulates are known agents of disturbance in many terrestrial ecosystems and have long been recognized for their substantial impact on ecological communities (Côté et al. 2004). Through their selective foraging behaviour, they induce significant changes in plant and animal communities (Rooney 2001, Rooney & Waller 2003, Horsley, Stout & Decalesta 2003, Côté et al. 2004). Some ungulates are even considered as keystone species due to their large impact on their local ecosystem (McShea & Rappole 1992, Rooney 2001). However, the effects are context dependent and vary over time and magnitude in response to a variety of factors, such as ungulate density, foraging intensity and habitat type (Côté et al. 2004). Many populations of ungulates, particularly deer, have increased in the northern hemisphere over the last decades as a result of fast population growth, relaxed hunting regulations and low predator pressure (Rooney & Waller 2003, Côté et al. 2004), and many conservationists are concerned about the ecological consequences that may follow this increase (Côté et al. 2004).

As ungulates interact directly and indirectly with the local fauna and flora, they have been identified as a conservation threat to some plant and animal species and in some cases, even whole ecological communities (Côté et al. 2004). This study aims to investigate the direct and indirect effects associated with ungulates foraging behaviour by focusing on the interaction between a common ungulate and a rare lepidopteran in Sweden.

Direct and indirect effects of ungulate browsing The main concepts of this study, direct and indirect effects, are used to describe the interactions of the study species, its host and a second herbivorous species. ‘Direct effect’ refer to one species affecting the presence and abundance of a second species by directly interacting with one another (Ricklefs & Relyea 2014). ‘Indirect effects’ occur when the interaction of the first and second species are mediated by a third intermediate species (Ricklefs & Relyea 2014). These two concepts will be described on an individual and community level in the following sections by taking examples from both plant and animal communities.

Ungulate-Plant interactions Direct effects of herbivory often refer to the herbivore-plant interaction, ungulate foraging on the plant tissue affecting plant morphology, growth, reproduction and survival (Russell, Zippin & Fowler 2001, Rooney & Waller 2003, Côté et al. 2004, Puentes & Johnson 2016). Plant species vary in their resistance and tolerance, as well as in their palatability and attractiveness, to herbivores. This variation ultimately determines the severity of the browsing on an individual plant species, in simple terms, palatable species of low tolerance and resistance will have difficulty to establish, grow and reproduce in heavily browsed environments (Rooney & Waller 2003, Knight, Caswell & Kalisz 2009), which will affect the long-term potential of the population to persist in the environment (Rooney & Waller 2003). This negative interaction has been observed in several forest ecosystems where ungulates - most studies focusing on moose and deer - have disturbed the development and regeneration capacity of trees by browsing on early life stages (Suominen, Danell & Bergström 1999, Russell, Zippin & Fowler 2001, Horsey, Stout & Decalesta 2003, Rooney & Waller 2003, Hegland, Lilleeng & Moe 2013). Intensive herbivory by ungulates has also been observed in other vegetation types, for instance understory vegetation (Rooney & Waller 2003). A study by Knight, Caswell & Kalisz (2009) investigating the effect of white-tailed deer (Odocoileus virginianus) grazing on the understory herb Trillium

5 grandiflorum in North America found that high grazing intensity altered the morphological properties of the plant population, which became dominated by small and non-flowering plants since the flowering stages were targeted by the deer. In conclusion, ungulate foraging can have significant consequences on individual plants and populations, which in turn can facilitate indirect effects on associated plant and animal communities (Rooney & Waller 2003, Côté et al. 2004).

The long-term effect of ungulate browsing can effectively change whole plant communities by altering the vegetation structure, abundance and species composition (Russell, Zippin & Fowler 2001, Rooney & Waller 2003, Côté et al. 2004, Puentes & Johnson 2016, Nishizawa et al. 2016). However, it is important to consider that the effect is dependent on browsing intensity and habitat type, and there is no uniform pattern or response (Côté et al. 2004, Knight, Caswell & Kalisz 2009, Hegland & Rydgren 2016, Nishizawa et al. 2016). Studies on ungulate browsing have found both negative (Urbanek et al. 2012, Puentes & Johnson 2016) and positive (Hegland, Lilleeng & Moe 2013, Nishizawa et al. 2016) effects on plant communities with varied browsing intensity and habitat type (Côté et al. 2004, Knight, Caswell & Kalisz 2009, Hegland & Rydgren 2016, Nishizawa et al. 2016). Browsing at intermediate levels can relax competition among species and allow less competitive species to establish and grow, which can yield higher species richness (Côté et al. 2004, (Hegland, Lilleeng & Moe 2013, Nishizawa et al. 2016). In contrast, overly extensive browsing can result in simplification of the vegetation structure and diversity, and reduce plant diversity and habitat complexity (Russell, Zippin & Fowler 2001, Côté et al. 2004, Knight, Caswell & Kalisz 2009, Urbanek et al. 2012).

Some plant species benefit from herbivory whereas others may suffer (Rooney & Waller 2003, Hegland & Rydgren 2016), which over time can develop to an alternative stable-state habitat (Rooney & Waller 2003). For instance, a study by Rooney & Waller (2003), examining the indirect and direct effects of white-tailed deer in mixed deciduous-coniferous forests in Wisconsin, found that high deer densities reduced the diversity of understory herbs and depressed the regeneration of individual tree species. This in turn increased the abundance of less palatable species, such as grasses, ferns and rushes (Rooney & Waller 2003). These effects of browsing on vegetation composition, abundance and structure as well as on individual plant morphology and quality, are not only affecting plant communities but also produce cascading effects on animal communities utilizing these plant resources (Côté et al. 2003). This thesis will focus on deer induced effects on insects, but it is important to consider that insects are not the only animal group affected by this damage, and other invertebrates, mammals and birds, are influenced as well (Côté et al. 2004).

Ungulate-Insect interactions Plants, being the base of terrestrial food webs, are important resources for herbivorous animals by acting as food, oviposition site and habitat (Prince 2002). Insect abundance and diversity are often associated with plant composition and structure (Seimann 1998, Haddad et al. 2001): higher species and structural plant diversity generally supports a more diverse insect fauna as a result of higher resource availability (Seimann 1998, Haddad et al. 2001). This plant-insect relationship can be explained by their long coevolutionary history, especially for phytophagous insects, with some species being specialists and other generalists on certain plant species or groups (Ehrlich & Raven 1964). When ungulate foraging alters the structure and composition of vegetation, one would expect this to have an impact on the insect communities (Kruess & Tscharntke 2002, Takagi & Miyashita 2014, Roberson et al. 2016).

Plant-mediated interactions, also denoted as “indirect effects” of herbivory (Gómez & González-Megías 2002), refer to the change in resource availability and hostplant quality in response to browsing, which consequently affect the local insect fauna (Kruess & Tscharntke 2002, Rooney & Waller 2003). By depleting the plant resources, herbivory can induce significant negative effects on the associated insects (Garner et al. 1996, Kruess & Tscharntke 2002, Lessard et al. 2012, Roberson et al. 2016), especially when considering specialized species dependent on a few specific plant resources for survival (Ehrlich & Raven 1964). Studies investigating browsing effects on insects have found substantial changes in both above- and below-ground arthropod communities (Lessard et al. 2012, Roberson et al. 2016), with examples of lepidopterans, saltatorians, hymenopteras (Kruess & Tscharntke 2002) and carabids (Garner et al. 1996) subjected to significant decreases in diversity and abundance due to extensive browsing pressure (Kruess & Tscharntke 2002). These alterations in the insect fauna are associated with reduced habitat complexity (Kruess & Tscharntke 2002) as increased browsing intensity decreases plant diversity (Garner et al. 1996, Gómez & Gonzáles-Megías 2002, Gómez & Gonzáles-Megías 2007, Robertson & Baltosser 2018). Hence, browsing- mediated changes to the availability of plant resources may have considerable effects on persistence of some insect species.

Browsing can also have significant effects on plant morphology, by changing its physical appearance it may reduce the plant attractiveness as a food resource and/or oviposition site (Pollard & Cooke 1994, Garner et al. 1996, Gómez & Gonzáles-Megías 2007, Robertson & Baltosser 2018). A study by Robertson & Baltosser (2018) found that browsing by the white- tailed deer on the Ozark Baltimore Checkerspot (Euphydryas phaeton ozarkae) primary hostplant caused a significant decrease in hostplant availability, as the butterfly actively avoided plants with visual browsing damage. Hence, browsing may indirectly affect the availability of potential oviposition sites for insects with specialized requirements on their host plant (Pollard & Cooke 1994, Robertson & Baltosser 2018).

Ungulates feeding on plant resources may not only affect the individual plants but also result in accidental consumption of eggs, larvae and/or adult insects (Baines et al. 1994, Dempster 1997, Gómez & Gonzáles-Megías 2002, Rooney & Waller 2003). This so-called incidental feeding (a direct effect) has been observed in some lepidopteran species (Baines et al. 1994, Pollard & Cooke 1994, Dempster 1997, Elmquist 2011), as well as in other insect groups (Gómez & Gonález-Megías 2007), and has even been considered as a threat to persistence of some species (Pollard & Cooke 1994). This interaction may come about when two species compete over the same resources, the ungulate feeding on plants that the insect uses as food, habitat and/or oviposition site (Pollard & Cooke 1994). An example of this conflict was illustrated in a study in Cambridgeshire on the effect of increased muntjac deer (Muntiacus reevesi) abundance on the white admiral butterfly (Ladoga camilla), the species sharing the same plant resource (honeysuckle Lonicera periclymenum, Pollard & Cooke 1994). The white admiral lays its eggs on the leaves and stem of the honeysuckle where they are at risk of being consumed by the deer. Since the butterfly population is declining this has been identified as a potential threat to the species persistence, but further studies are needed (Pollard & Cooke 1994). Another example comes from Sweden where roe deer (Capreolus capreolus) have been observed foraging on host plant of the rare lepidopteran chequered blue (Scolitantides orion, Elmqvist & Carlsson 2009, Gothnier & Jaramillo 2019). This has been identified as a potential conservation issue for the species, but the extent to which this affects the population is not yet known.

Scolitantides orion S. orion has experienced significant decrease in population size all across Sweden, as well as in the other Nordic countries, over the past decades (Elmquist 2011, Marttila et al. 2000) and is classified as endangered (EN) according to the Swedish species red-list (SLU Artdatabanken 2020). The main reasons for its decline are habitat loss, as a result of reduced grazing and habitat succession and habitat fragmentation (Marttila et al. 2000, Elmquist 2011). But it has also been suggested that incidental feeding by roe deer, through sharing the same plant resource: orpine (Hylotelephium telephium), could be a potential conservation threat to S.orion persistence (Gothnier & Jaramillo 2019, Elmqvist & Carlsson 2009, Elmquist 2011). In a survey by Elmqvist & Carlsson (2009) documenting the presence of S. orion in 18 localities of Södermanland county between 2005-2008, identified damage on orpines and loss of both eggs and larvae due to deer foraging. Some of the orpines that were observed inhabiting eggs or larvae earlier in the season were later, at the second visit, found damaged or fully consumed by deer (Elmqvist & Carlsson 2009). However, the effect of roe deer damage varied among the localities, with sites more accessible to deer were generally more affected (Elmqvist & Carlsson 2009). For instance, in one of the localities (denoted as locality 8 in their study) they observed substantial roe deer impact in 2008, with nearly all plants had been consumed. This locality had from previous inventories 2005-2007 documented findings of both eggs and larvae. But in 2008 no eggs were found as a result of roe deer foraging. Eggs were instead found on a locality ten meters from the initial site, where there have not been any sightings previously, at least in this survey. Evidently, it seems that roe deer can induce changes in S. orion egg laying behaviour by altering the availability of their plant host (Elmqvist & Carlsson 2009).

Similarly, roe deer damage on orpine plants was found in inventories in Stockholm archipelago (Gothnier & Jaramillo 2019). An S. orion inventory conducted by Gothnier & Jaramillo (2019) on Mörtö island in the Stockholm archipelago, observed a large quantity of plants (approximately 50-70) that had been consumed by roe deer. Since Mörtö is one of the few localities in Sweden inhabiting S. orion, there have been concerns of what consequences roe deer foraging on orpine plants may endure on the population. However, the consequences of roe deer browsing on orpine on the Swedish population of S. orion have yet not been studied in detail (Gothnier & Jaramillo 2019). The question remains whether roe deer foraging is a threat to the S. orion persistence and if this is something that should be considered in the conservation efforts for the species in Sweden. Objective My objective in this master thesis was to analyse the indirect and direct consequences of roe deer foraging on the S. orion population in Mörtö island, in the Stockholm archipelago.

Indirect effects I studied indirect effects by comparing the host plant abundance and quality prior and at the onset of the butterfly season, to determine whether the butterfly’s oviposition behaviour is affected by any eventual damage caused by roe deer browsing.

Direct effects I studied direct effects by evaluating how many eggs and larvae were consumed over the season to determine how the browsing pressure affects the early stages and ultimately the whole population of the butterfly.

Protected vs. unprotected plants I established experimental cages to compare any eventual difference in abundance of butterfly eggs and larvae between protected and unprotected plants. An additional aim was to determine if the cages are an effective way to protect plants from roe deer induced damage.

Hypothesis • Fewer eggs and larvae will be found on plants that show visible signs of browsing, either by direct consumption of eggs and larvae or indirectly by being a less attractive host choice. • Fewer eggs and larvae will be found on the plants that do not have any browsing protection.

Material & Methods Study species Scolitantides orion, or the chequered blue, is a lepidopteran species of high conservation concern in Northern Europe, where it has been recognized as one of the rarest lepidopterans in Sweden, Norway and Finland (Eliasson et al. 2005). Its current distribution in Sweden is restricted to a limited number of sites located, with a patchy distribution, in Bohuslän, Dalsland, Västergötland, Östergötland, Södermanland and Uppland (Elmquist 2011). It prefers open and sun-exposed bedrock habitats with abundant availability of orpines (Hylotelephium telephium; Figure 1), their primary host species during the egg and larval stage, as well as members of the plant genus Sedum (Elmquist 2011, Carlsson & Elmqvist 2013, Marttila et al. 2000). S. orion was long thought of having disappeared from Stockholm archipelago. Despite many inventory efforts in the early 21th century, neither eggs, larvae nor adults were found in localities where the species was found in the past (Gothnier & Jaramillo 2019). In 2018, the species was rediscovered in a historically important site: Munkö, but only a few eggs were found (Gothnier & Jaramillo 2019). However, S. orion was also discovered in a new locality: Mörtö, with documentations of eggs, larvae and flying imagoes (Gothnier & Jaramillo 2019). It has now been recognized as an established species on the island.

Figure 1. The left figure shows an example of habitat, sun-exposed bedrocks, preferred by S. orion and their primary host species orpine (H. telephium) on the right. All pictures and illustrations in this thesis have been taken/made by A. Johansson.

S. orion is a small butterfly with a wingspan of 2.7-3.2 cm. It is recognized by its blue and grey- fused upper-wings with black and white spots colouring the edges of the wings (Figure 2a & 2b, Elmquist 2011). Their main visual characteristic, separating it from other similar looking species, is the black dotted pattern on white background on the underside of the wings with a species-unique orange band stretching vertically alongside the wing (Figure 2b, Elmquist 2011).

a b

Figure 2. Picture of S. orion. Adult butterflies emerge from their overwintering pupae in early May to late June, depending on the geographical location, and are often seen flying during sunny and calm weather conditions (Elmquist 2011). Adult S. orion can be quite difficult to spot since their activity is highly weather dependent and limited to a restricted time-period during the day (Elmquist 2011). The oviposition activity starts shortly after the mating period (Elmquist 2011) and females oviposit their eggs on the stem and leaves of orpine (Elmquist 2011, Carlsson & Elmqvist 2013, Marttila et al. 2000). Carlsson & Elmqvist (2013) observed that S. orion can oviposit their eggs on stonecrops (Sedum album), but this came at a cost of reduced larval survival and lower body condition as compared with individuals growing on orpine plants. This confirms that orpine is the preferred host plant for S. orion (Carlsson & Elmqvist 2013).

The egg development takes two to three weeks and the emerging larvae use the hostplant as a food resource during the rest of the larval stage (Eliasson et al. 2005, Carlsson & Elmquist 2013). If necessary, the larvae have the capacity to move between host plants (Carlsson & Elmquist 2009). The larval stage lasts for about a month before they enter the pupal stage, at which they overwinter over one, but sometimes even two, winter seasons (Eliasson et al. 2005, Carlsson & Elmquist 2013) and emerge at the beginning of the next mating period.

Study site The study was conducted in Mörtö (59°9.6′N, 18°38′E) located in Värmdö municipality, Stockholm county, during the spring and summer season of 2020. Mörtö is a 187ha island consisting of mixed pine-forest, calcareous bedrocks and other open vegetation habitats. The vegetation of these outcrops is characteristic for calcareous soils (Källgård 2013) including, for instance, the elder-flowered orchid (Dactylorhiza sambucina) and fragrant orchid (Gymnadenia conopsea). Some residential and summer cottages are located in the island.

The study site was restricted to the northern and central part of the island (Figure 3), an area owned by Mörtöstiftelsen. This area was further confined to include only exposed bedrocks and

10 sites where S. orion eggs, larvae and adults have been documented in the 2018-2019 inventories (Gothnier & Jaramillo 2019). This confinement is based on S. orion habitat preference, as it prefers open bedrock meadows with abundant orpine populations (Eliasson et al. 2005). The chosen area was then divided into six subsites (A-F) based on their geographical location.

Figure 3. Cartographic representation over Mörtö island with land use information, height and depth curves, walking trails, settlements and the study site (highlighted in red). The map has been computed by using QGIS version 3.6.0 and the data has been derived from Lantmäteriet [retrieved: 2020-02- 10]. Experimental set-up Orpine inventories First, I conducted an orpine inventory to estimate the abundance of hostplants within the defined study area. The first orpine inventory was carried out during the 9-11 of May 2020, prior to butterfly activity season, by systematically walking through each subsite and documenting all found orpines. All plants I found were counted, GPS-tracked and ID-marked by attaching floral sticks, with an ID-note, into the soil close to the plant. Plants occurring in clusters, and hard to distinguish from each other, were individually marked by attaching small cable ties (2.5 x 100mm), with an ID-note, at the bottom of the stem (Figure 4a). To avoid any visual interference during the butterfly’s oviposition activity, I hid the notes underneath soil and other substrate (Figure 4b). The second orpine inventory was carried out at the onset of the butterfly activity, 1-3 of June 2020, the same way as described above with the addition of counting the number of eggs found on each plant.

Deer exclusion cages To determine the effects of deer browsing on the butterfly’s hostplant, I established a total of 47 experimental cages within the study area, to protect a proportion of the plants from deer- caused damage. I constructed the cages using poultry netting, made up by galvanized steel with hexagonal gaps, clipped and reformed into cubes and anchored to the ground by using two 90

11 cm long wooden sticks (Figures 4c & 4d). The cage measurements were approximately 27 cm high, 45 cm long and 34 cm wide, with a small variety in size and form depending on where it was established. The mesh size was 4 cm so that the butterflies (wingspan of 2.7-3.2 cm; Eliasson et al. 2005) could move freely in and out from the cage.

In 9-11 April 2020, I installed the 47 experimental cages within the defined study area. The cages were proportionally distributed among the six sites, to get a roughly similar percentage of protected plants in each site. Sites with a higher number of orpines found during the inventory had more cages than sites with a lower plant abundance. The cages were thereafter randomly distributed, using a random number simulator, within each individual site. However, nearly all cages were relocated on the 1-3 June 2020, since only a few of the cages had plants with eggs. I made the decision of moving the cages in consultation with my supervisors. A decision to not move the cages would have compromised the purpose of my study, which was to compare the change in number of eggs/larvae on protected and unprotected plants. A lower number of eggs and larvae on protected plants would have meant that roe deer impact could not have been evaluated. Since this was done early on the butterfly season, it did not have a significant effect on the outcome of the study. However, this also means that the result is somewhat biased which needs to be considered in the results and discussion. The relocation of the cages was done similarly as described above, by first distributing the cages among the sites proportionally (based on the number of plants having eggs on them) and then randomly distributing the cages among the plants with eggs using the same number simulator. The cages remained on the same spot throughout the rest of the study.

a b b

c d d

Figure 4. Images illustrating the experimental set-up, with the ID-marking represented in 2a & 2b and the experimental cages in 2c & 2d.

Data collection 1. Estimating indirect effects of deer and other damage To get an estimate how deer indirectly affect the butterfly population by feeding on their hostplant, the abundance and quality of orpines were estimated prior and at the offset of the butterfly oviposition activity (Figure 5d). Prior to this activity the host abundance was estimated on May 9-11 by counting the number of plants, and the individual host quality was assessed by measuring the height, counting the number of leaves and documenting any visible damage on the plant. The damage on each plant was examined visually and based on how much of the plant that had been lost as well as the shape of the damage. I used this information to conclude what had caused the damage. For instance, the damage in plants that had their stem clearly “cut off”, as shown in Figure 5a, was estimated being a result of deer foraging. Those plants observed being eaten by Apollo butterfly larvae (Parnassius apollo, Figure 5b) were classified as Apollo foraging.

Plants with bitemarks on the stem and/or leaves (Figure 5c) were documented as a result of unknown insect herbivory. The observations were documented in a field protocol shown in Table 1. The same measurements were taken at the onset of the butterfly activity on 1-3 June and compared with the “before” data, to determine whether deer indirectly affect the butterfly’s oviposition by altering host plant availability and quality.

a b c

Figure 5. Figures representing observed damage caused by a) deer, b) Apollo, c) unknown insect. d) Graphical representation of the experimental implementation estimating the indirect effects of deer damage.

Table 1. Representation of the field protocol used to describe the orpine plant ID, site location (A-F), coordinates (coordinate system: WGS 84), presence of a cage (Yes/NO), the height of the plant, number of leaves and if any visible damage. ID Site Coordinates Cage (Y/N)? Height Nr. leaves Visible (cm) damage?

1 A 59.16385 N, 18.63911 E Y 7.5 13 No

… … … … … … … 23a B 59.16364 N, 18.63828 E N 3 6 No 23b B 59.16364 N, 18.63828 E N 3.5 9 No … … … … … … … 66 D 59.16145 N, 18.63234 E N 2 0 Plant eaten

2. Estimating direct effects of deer damage Direct effects of deer browsing were examined by conducting butterfly inventories at five different occasions over the season: at the onset of the butterfly activity (1-3 June), then each week the following two weeks (8-10 June, 15-17 June), when the eggs had entered the larval stage (22-23 June) and finally at the end of the larval stage (29 June – 1 July) (Figure 6).

a b c

Figure 6. a&b) Picture of orpine and S. orion eggs, c) Picture of a S. orion larva d) graphical representation of the egg and larva counting throughout five weeks to estimate direct effects of deer damage. The butterfly survey was accomplished by counting and documenting eggs and/or larvae on each identified plant. The eggs, approximately 0.5 mm in diameter (Elmquist 2011), were recognized by their white colour and rounded shape with a small hollow centre (Figure 6a&b, Eliasson et al. 2005). Larvae of S. orion are green, resembling the colour of orpine, with a distinct red-brown band on their back and diffused red-brown colour at the edges of their body (Figure 5c, Eliasson et al. 2005). The inventory methodology was based on an information

14 guide by County Administrative Board of Stockholm (2019), concluding that egg and larva surveys are the most efficient method for a population estimate of S. orion. Adults have consequently not been included in this survey, since reliable surveys are difficult to conduct in the field and do not give a good estimate the population size.

The direct effects of deer browsing were estimated by calculating how many eggs and larvae had disappeared over the season as a result of deer herbivory, insect herbivory or other reasons such as diseases, predators or parasites (Elmquist 2011). This was done by comparing the egg quantity at the beginning of the butterfly season and at the end of the butterfly season. I estimated the reason for egg loss by comparing the damage data (what has caused the damage, as described above) with the number of lost eggs on each plant, which were quantified by comparing egg number between inventories. For instance, if X number eggs were lost between two inventories, I compared this information with my documentations of damage, to examine what might have caused the egg loss. If the damage reports demonstrated that roe deer, P. apollo or an unknown insect had foraged on the plant at this time, I estimated the loss to be a result of that damage identity. However, eggs that had disappeared and the reason could not be derived from any of the damage reasons above, were documented as “other reasons”. This category includes eggs that had fallen off the plant, remained undeveloped or snatched by a predator or parasitoid (Elmquist 2011) or other random events.

3. Protected plants vs. non-protected (control) plants The control plants, with no experimental protection, were compared with protected plants to determine if there was any difference in number of egg and/or larvae between the two groups.

Figure 7. Graphical representation of the comparison that was made between protected and unprotected plants.

Analyses Data preparation The raw data were prepared prior to the analyses to exclude incomplete inventory data, for instance plants with missing markings or plants that had died. Out of the 1327 plants that initially was included in the survey, a total of 1310 plants were used in the analysis. Based on these raw data, the following parameters were recorded:

Egg max The maximum egg number observed on each plant throughout the study period.

Damage The plants that have been exposed to any form of damage, independent of the magnitude. For instance: bitemarks on leaves, stem or meristem; loss of leaves; whole plants consumed, etc. This category was divided into four subcategories, see “Damage ID” below.

Damage ID The identity of what caused the damage on the plant, identified into: Deer (Roe deer), P.apollo (Apollo butterfly), Unk.insect (unknown/unidentified insect) and 0 (no visual signs of damage).

Damage percent Percentage of how much of the plant tissue has been consumed. Calculated by comparing plant height and number of leaves (nr. leaves) between the first and the second inventory (inv.x) and weighted equally (×0.5). For instance, if Plant height (inv.1) > Plant height (inv.2) and Nr. Leaves (inv.1) > Nr. Leaves (inv.2), then plant tissue has been consumed:

(height푖푛푣.2 − height푖푛푣.1) × 0.5 − (nr. leaves푖푛푣.2 − nr. leaves푖푛푣.1) × 0.5 = Percentage plant loss

All following statistical analyses were calculated using RStudio Team (2020).

1. Host preference Caged plants were excluded from these analyses (137 plants in total), comparisons were made only among the control plants (1173). Since number of plants with (278) and without (895) eggs were highly unequal, 278 of the plants without eggs were randomly selected to make a fair comparison between the groups.

1.1 Host preference: Egg Y/N vs. plant morphological traits The first test was used to evaluate whether plant morphological traits (plant height and number of leaves) influenced host plant choice by S. orion. This was done using a generalized linear mixed model analysing the nominal response variable: egg presence (Egg Y) and absence (Egg N), with the two continuous predictor variables: plant height and number of leaves, with “Site” (A, B, D, E, G, H, I) as a random factor. Both numeric variables were square root transformed to fit a normal distribution. A generalized linear mixed model serves to estimate correlations among data with both fixed and random factors, with a categorical response (Jiang 2007).

1.2 Host preference: Egg max vs. plant morphological traits The second test evaluated whether plant height and number of leaves determine how many eggs will be oviposited on a plant. A linear mixed model was used to analyse the numeric response variable Egg max with the numeric variables Plant height and Nr.Leaves, with Site as a random effect. All numeric variables were square root transformed prior to analysis to fit a normal distribution. The linear mixed model estimates correlations among data with both random and fixed factors but with a continuous response variable (Jiang 2007), unlike the general model described above.

2. Indirect effects Only the control plants (1173) were used to test the indirect effects of damage ID on S. orion oviposition behaviour, hence excluded the caged plants in these analyses. To get a better representation of the relationship between number of eggs and plant damage, all data on plants having neither eggs nor damage were removed prior to the analysis (542 data points). The risk of including these data is that the effect of the analysis is weighted down by multiple zeros.

2.1 S. orion egg-laying behaviour: does plant damage (Y/N) have an effect? To evaluate if the number of eggs on plants (egg max) differs in response to damage (binominal: Y/N) a nonparametric chi-square test (Kruskal-Wallis) was applied. The non-parametric version of the chi-square test was used since egg max contained multiple zeros (plants with no eggs), and the data could not reach a normal distribution despite several transformation attempts. This test is a non-parametic version of one-way ANOVA which is used to evaluate the differences between independent groups (Chan & Walmsley 1997).

2.2 S. orion egg-laying behaviour: does plant damage (percent) have an effect? A Spearman’s rank correlation was computed to determine whether damage percent affects S. orion egg-laying behaviour (number of eggs on each plant, egg max). The damage percent (see Data preparation) was calculated as described above. However, since the object is to estimate the plant quality at oviposition, I only included damage prior to the plant had its maximum number of eggs. A Spearman’s rank correlation is used to test whether the two variables are associated with each other and the strength of that eventual correlation, with the analysis based on a ranked dataset (Gauthier 2001).

4. Caged vs. uncaged plants Out of the 1310 plants in the study, 137 (10.4%) were experimentally protected (caged) and 1173 (89.6%) were unprotected (control). Due to skewed data distribution between the two groups, 450 plants, comprised of both caged and control plants, were selected and divided into 44 subplots, based on their geographical location. The data selection was computed in the geographical information system software: QGIS 3.6.0, with each subplot containing one cage (comprising the caged plants) and several surrounding control plants (Figure 7). The subplots were chosen so that the ratio of control:caged plants was about 2:1 or 3:1, depending on the location. During the data selection, each point (representing one plant) was represented as a black dot with no information about plant properties (for instance, ID, plant quality, number of eggs, etc.) to avoid a biased selection. However, since plants were selected based on their geographical location in relation to each cage, they were not randomly selected and, in that sense, geographically biased. Distance from the cage to a control plant was maximum 16.7 m, minimum 0.5m and mean 5.17m. However, these values were influenced by site I, where distances between plants were longer. In total, 299 control and 137 caged plants were used in the analyses.

Figure 8. Graphical illustration of the data selection method within QGIS 3.6.0, where caged plant and control plants surrounding the cage were treated as one unit (subplot). Plants outside the selected area were excluded from the analysis. 4.1 Comparison between control vs. caged plants: egg total A mixed-model ANOVA, using the lmerTest-package, was used to analyse the difference in total egg number per plant (at plant Egg max) between caged and uncaged plants. Egg max was treated as a response variable, Cage (binominal: Y/N) as treatment, subplot and site as random effects. A mixed model ANOVA is used to test for any eventual differences between the treatment groups, with both fixed and random factors (Murrar & Brauer 2018). The model was partly nested with subsites within the sites.

4.2 Comparison between control vs. caged plants: egg survival Egg survival was calculated by dividing Egg max with the final egg number (at the last inventory F4), to get the percentage estimate how many eggs were lost. Plants that had no eggs throughout the inventory period were excluded from this analysis. Egg survival was analysed with a mixed-model ANOVA, using the lmerTest-package, with Egg survival as response variable, Cage (binominal: Y/N) as treatment, subplot and site as random effects. The model was partly nested with subsites within the sites.

4.3 Comparison between control vs. caged plants: damage percent The damage percent caused by DamageID was calculated as in the previous analyses, only this time all inventories were included, representing the overall damage throughout the season. The damage percent of the plants which had been damaged more than once during the inventory period (26 /450 plants) were added together. A beta regression model, using the betareg- package, was used to test the difference in damage percent between control and caged plants. The beta regression model is a logistic regression model that allows percentages to be analysed, the only drawback is that it cannot treat “extreme” values: 0 and 1 (Cribari-Neto & Zeileis 2010). To compensate for this problem, I used the following transformation (Smithson and Verkuilen 2006):

(푦 × (푛 − 1) × 0.5)/푛, where y is the percentage values and n is the total plant number (450).

Results Out of the 1310 plants included in the study, 359 (27,40%) had at least one S. orion egg laid on the stem or leaves. The maximum number of eggs added up to 827 (egg max) over the whole season and study area, with a spatial and temporal distribution shown in Table 2 and Figure 8.

Table 2. Total number of egg and larvae found at each inventory time in site. “Egg max” is the sum of maximum number of eggs found on each plant.

Site Eggs/Larvae P1 F1 F2 F3 F4 Egg max A Egg 13 24 41 59 53 76 Larva 0 0 0 0 6 B Egg 4 25 28 25 17 40 Larva 0 0 2 2 3 D Egg 13 87 185 218 187 276 Larva 0 0 0 2 15 E Egg 42 104 144 154 152 230 Larva 0 0 0 2 10 G Egg 2 17 40 31 20 58 Larva 0 0 0 0 0 H Egg 0 8 27 48 43 55 Larva 0 0 0 0 4 I Egg 0 51 61 54 47 92 Larva 0 0 0 0 1 Total Egg 74 316 526 589 519 827 Larva 0 0 2 6 39

Figure 8. Map representing the egg distribution (at egg max) over the seven sites (A, B, D, E, G, H, I) at central and eastern Mörtö island. A map made by A. Johansson.

1. Host preference 1.1 S. orion host selection based on plant morphology, egg(Y/N) The generalized linear mixed model found a significant (p<0.001) effect of Nr.leaves and egg presence (Table 3), indicating that S. orion more often choose to lay their eggs on host plants with a larger number of leaves (Figure 9b). However, no significant effect was found between egg presence and plant height (p >0.05), nor in the interaction between the two variables (p >0.05). These results contradict Figure 9a, where a visual examination of the boxplot indicates that more eggs are found on larger plants. This visual observation was in fact confirmed when individually testing height and egg presence in a generalized linear mixed model (p <0.05). This suggests that plant height in the first model is overridden by the effect of leaf number.

Table 3. Results from the generlized linear mixed model (fit by maximum likehood, Laplace Approximation).

Fixed effects Estimate Std. error z-value P (intercept) -2.53043 0.85828 -2.948 0.003196 ** sqrtHeight 0.08058 0.33283 0.242 0.808700 sqrtNrLeaves 0.89379 0.25437 3.514 0.000442 *** sqrtHeight * -0.01075 0.08861 -0.121 0.903403 sqrtNrLeaves

Host preference: egg presence & plant trait a. b.

Plant height (cm) Number of leaves

Egg presence Egg presence Figure 9. Graphical representation of the plant height (a.) and number of leaves (b.) distribution between plants with (Egg presence=Y) and without (Egg presence=N) eggs. 1.2 S. orion host selection based on plant morphology, Egg max The linear mixed model (Table 4) found a significant positive relationship (p<0.05) between number of leaves and number of eggs, confirming the results in section 1.1 (Figure 10b). No significant results were found between plant height and number of eggs (p-value<0.05, Table 4), despite a clear visual trend, Figure 10a. The interaction between the two variables (Nr. leaves and Plant height) was, however, significant (p<0.05). These results suggest that the effect of plant height depends on the number of leaves, S. orion preferring to deposit their eggs on taller plants with a higher number of leaves.

Table 4. Results from the Linear mixed model fit by REML, Satterthwaite’s method.

Fixed effects Estimate Std. error df z-value Pr (intercept) 0.23541 0.18280 13.26290 1.288 0.21982 sqrtHeight -0.11498 0.06703 546.58469 -1.715 0.08687 sqrtNrLeaves 0.15489 0.05258 547.71591 2.945 0.00336* sqrtHeight * 0.04612 0.01723 546.25936 2.677 0.00766* sqrtNrLeaves

Host preference: egg number & plant trait

a. b.

Sqrt(egg number) Sqrt(egg number)

Sqrt(plant height) Sqrt(number of leaves) Figure 10. Plot representing the correlation between egg number and height and number of leaves, separately, all data is square root transformed (sqrt). 2. Indirect effects 2.1 S. orion egg-laying behaviour: does plants damage (Y/N) have an effect? Out of the 1173 uncaged plants (hereafter referred to as control plants), a total of 388 (33,1%) plants were exposed to damage at least once during the study period (Table 5). Most plants were damaged by unknown insects (332), but some plants were fully or partly consumed by deer (54) or P. apollo (23). The magnitude of damage (calculated in percent) caused by damage ID varied greatly among the three groups (Figure 11).

Table 5. Number of plants at each study period that has been exposed to damage by Unk. Insect, Deer or P. apollo. The “Total plants” indicates how many plants, independent of inventory time, have been damaged in any of the categories. Plants that have been damaged multiple times throughout the study period are counted as one.

Unk.insects Deer P. apollo Inventory total I1 58 1 0 59 F1 166 21 19 206 F2 134 8 9 151 F3 108 12 7 128 F4 68 13 2 82 Total plants 332 54 23 388

Damage percent (%) by Damage ID

Damage (%)

P.apollo Deer Unk. insect Damage ID Figure 11. Percentage plant damage (0-100%) throughout the study period caused by damage ID: Parnassius apollo (P. apollo), deer and unknown insect (Unk.insect). Plants that have been damaged multiple times are separated as two (or more) in separate rows. 2.2 S. orion egg-laying behaviour: does plants damage (in percent) have an effect? Overall, plants with no visible signs of damage were preferred as hosts. Out of the 278 control plants with at least one egg on the stem or leaf, 226 were undamaged and 52 were damaged prior to oviposition (Table 6). This resulted in a highly significant (p<0.0001, df=1, chi- squared= 417.15) difference in egg number between undamaged and damaged plants (Figure 12). Table 6. The table represents the distribution in control plants with at least one (≥1) or no eggs that have been exposed to “No damage” or “Damage (All ID)”. The Damage ID was classified into three groups: Unknown insects, P. apollo or Deer.

Damage ID No Damage Unk. P.apollo Deer damage (All ID) insect Eggs (≥1) 226 52 44 3 5 No eggs 542 353 295 19 39

Egg number & plant damage

Egg number

none Damage Plant damage Figure 12. Egg distribution between undamaged (none) and damaged plants. 2.3. S. orion egg-laying behaviour: is plant quality affecting oviposition behaviour? The Spearman’s rank correlation indicates a significant negative correlation (p-value<0.05, rs= -0.7548294) between the number of eggs and the percentage of damage (Figure 13). This indicates that the quality of the plant has an impact on host plant choice of S. orion.

Egg number & damage percent (%)

Egg number

Damage (%) Figure 13. Plot describing the correlation between number of eggs (Nr.eggs) and percental plant damage (Damage %).

3. Direct effects The total number of eggs lost over the whole season in the control plants was 255: 4 lost to P. apollo, 16 deer, 38 Unk.insect and 197 for other reasons (Table 7). Evidently, S. orion is experiencing accidental predation at the very early stages of its lifecycle. However, most of the eggs were lost due to other reasons that could not be explained by this study and evidently there is a knowledge gap on what happens to many of the eggs during the season.

Table 7. The number of eggs disappearing between inventories that could be derived back to damageID: P. apollo, deer, unk. Animal and *other (could not be derived from any of the identified damage reasons).

P. apollo Deer Unk.Insect *other i1-f1 0 0 0 0 f1-f2 0 2 (2/59) 10 (10/59) 27 (27/59) f2-f3 3 (3/404) 9 (9/404) 19 (19/404) 95 (95/404) f3-f4 1 (1/455) 5 (5/455) 11 (11/455) 75 (75/455) Total 4 16 40 197

4. Control (uncaged) vs. experimentally protected (caged) plants Overall, there was a higher number of eggs laid on the control plants (278 plants with ≥1 egg and 632 in total) compared to caged plants (81 plants with ≥1 egg and 195 in total, Table 8). However, higher proportion of the eggs were laid on the caged (59%) than on control plants (24%). But these differences can partly be explained by how the caged plants were selected at P1, as plants with at least one egg were chosen to be caged.

Table 8. Summary of the caged and uncaged data: number of plants, egg total (at egg max), number of plants where egg>0 and number of plants where egg = 0.

Treatment Number of plants Egg total Number of plants where Number of plants where egg = egg > 0 0 Caged 137 195 81 (59%) 56 Control 1173 632 278 (24%) 895

In total, 255 eggs on control plants and 70 eggs on caged plants were lost throughout the study period (Table 9). Hence, it seems that the cages were an effective protection against deer and P. apollo, since no eggs were lost due to these reasons. However, the cages were not very effective against unidentified insects and other unknown reasons. The larvae of P. apollo could move freely in and out of the cage (this was observed during the study); this observed pattern is probably due to random chance. But evidently deer were effectively stopped from consuming the eggs when a cage was present.

Table 9. Loss of eggs throughout the season due to damage ID (P. apollo, Deer, Unk.insect and *other, a comparison between control and caged plants.

Control Cage Inventering P. apollo Deer Unk.insect *other P. apollo Deer Unk.insect *other f1-i1 0 0 0 0 0 0 0 0 f2-f1 0 2 10 27 0 0 3 20 f3-f2 3 9 19 95 0 0 6 18 f4-f3 1 5 11 75 0 0 1 22 Total 4 16 40 197 0 0 10 60

4.1 Comparison between control vs. caged plants: egg total The mixed model ANOVA found a significant difference in total egg number per plant between caged- and control plants (p<0.05, df=1, F=5.6001, Table 9). This indicates more eggs per plant were found on caged plants (Figure 14). This result may however be biased since the placement of the cages at the first inventory (F1) was chosen among plants where at least one egg was present (but randomly chosen among the egg plants).

Table 10. Summary of the Analysis of Variance Table, Satterthwaite’s method.

Sum Sq Mean Sq NumDF DenDF F-value P Cage 10.777 10.777 1 29.361 5.6001 0.02477*

Egg number & treatment

Egg number

control Caged Treatment Figure 14. Number of eggs distributed on control and caged plants.

4.2 Comparison between control vs. caged plants: egg survival A comparison of eggs between caged and control plants throughout the study shows that 39 caged plants (73 eggs in total) and 47 uncaged plants (102 eggs in total) experienced egg loss (Figure 15a). The control plants seem to have lost roughly one egg throughout the season, whereas in caged plants the median is zero. The mixed model ANOVA comparing the proportional egg survival found no significant difference between the two groups (p>0.05, Df=1, F = 0.0296, Table 11, Figure 15b).

Table 11. Summary of the Analysis of Variance Table, Satterthwaite’s method.

Sum Sq Mean Sq NumDF DenDF F-value P Cage 0.0048967 0.0048967 1 25.379 0.0296 0.8648

Egg loss over the season Proportional egg survival & treatment

a. b.

Egg survival (%) Egg lost

control Caged control Caged Treatment Treatment

Figure 15. The left figure (a.) shows how many eggs that has gone lost throughout the season between the control (grey bar) and caged (white bar) treatments. The figure on the right (b.) illustrates the proportional egg survival throughout the season from 1.0-0.0 between the control (grey bar) and the caged (white bar) treatments. A value of 1.0 means that no eggs have disappeared and a value of 0.0 means that all eggs have disappeared. 4.3 Comparison between control vs. caged plants: damage percent The results from the beta regression analysis indicate a significant difference between the two treatments (p<0.05, Std.Error=0.04488, z =15.49). Control plants were exposed to a higher risk of being consumed by herbivorous animals (Figure 16), suggesting that the cages were an effective protection against herbivory.

Percentage damage (%) & treatment

Damage (%)

Control Treatment Caged

Figure 16. Percentage damage on each individual plant throughout the study period, comparing control and caged plants. Discussion Scolitantides orion is of high conservation concern in Sweden, where it has been classified as endangered (EN) according to the Swedish red-list (SLU Artdatabanken 2020) and sighted on only a few localities throughout the country (Elmquist 2011). The main threat to the species persistence is loss and fragmentation of their sun-exposed bedrock habitats as a result of increased vegetation overgrowth (Marttila et al. 2000, Elmquist 2011). There are also concerns regarding roe deer’s impact on S. orion by foraging on their hostplant, the orpine (Elmqvist & Carlsson 2009, Elmquist 2011, Gothnier & Jaramillo 2019). However, the magnitude of threat roe deer poses on the butterfly population has not been studied formerly until now (Gothnier & Jaramillo 2019).

The objective of this study was to analyse the indirect and direct effects of roe deer browsing on the S. orion population in Mörtö, Stockholm archipelago. The conclusion from this study was that roe deer could not be confirmed having a significant effect on the S. orion population, in Mörtö. Overall, 54 plants were identified being damaged by deer and 16 eggs in total were consumed in the process, in comparison with the 1310 plants and 827 eggs (egg max) found throughout the study period, this cannot be considered a significant amount. Hence, roe deer may not pose as a large threat to S. orion population in Mörtö as initially expected. The current population size of roe deer in Mörtö is, to my knowledge, unknown. However, I found some evidence of roe deer inhabiting the island: I saw one female, heard at least two roaring males and found newly laid faeces at nearly all the localities. Evidently roe deer inhabits Mörtö, but the population might not be dense enough to pose a threat to the island population of S. orion. This does not however rule out the possibility that roe deer pose a threat to the species persistence in other, more exposed localities. Roe deer has been found consuming eggs and larvae in other localities in Sweden, for example in Strängnäs (Elmqvist & Carlsson 2009). Hence, it may still be essential to consider roe deer as a potential threat to the species during their most vulnerable life-stages, especially considering more exposed habitats and less viable populations.

While this study did not demonstrate roe deer having a significant browsing effect on S. orion population, there are some interesting observations derived from the data that will be discussed in the following sections.

Indirect effects Female host plant selection for oviposition is an essential process in lepidopterans, as it affects both offspring performance and survival, as well as the female reproductive success (Refsnider & Janzen 2010). S. orion oviposit on two main host plants: orpine and white stonecrop (Sedum album) (Elmquist 2011). However, studies of S. orion have found developmental defects and increased mortality following breeding trials on the white stonecrop (Tränker & Nuss 2005, Carlsson & Elmquist 2013), indicating that the species is not an optimal host plant. Indeed S. orion prefers orpines when a choice is offered (Tränker & Nuss 2005). Female S. orion host choice is also influenced by other factors such as temperature, and females often deposit their eggs on shady spots to avoid direct radiation (Elmquist 2011). This study analysed whether individual plant properties and quality affect S. orion host choice for oviposition, which I will discuss in the following section.

The results of this study indicate that females of S. orion are selective in their host plant choice for oviposition. Plant properties such as plant height and number of leaves, as well as plant quality seem to affect their choice of plant host. I found that female preference was higher for taller plants with a larger number of leaves, where significantly more eggs were found on plants exhibiting these traits. Previous studies have documented female preference for small and drought stressed plants and they rarely oviposited on large plants (Elmquist 2011). However, since orpines can grow up to 50 cm in size (Mossberg & Stenberg 2007) and most plants in this study were relatively small (with a mean height of 7 cm and maximum 22cm), this may not be a contradicting result. Hence, these physical plant properties seem to be a contributing factor to S. orion host plant selection.

Another interesting result was that host choice seems to be dependent on plant quality, with females avoiding oviposition on plants with visual signs of damage. This behaviour suggests that roe deer, as well as other insect herbivores, indirectly alter availability of suitable plant hosts for S. orion oviposition. This means that the first hypothesis stating ‘fewer eggs will be found on plants with visible signs of browsing’ was confirmed. This herbivory effect is not solely a result of deer browsing, but also due to other herbivores such as the Apollo butterfly and other (unidentified) herbivorous insects utilizing the same plant resource. The red-listed Apollo butterfly was found feeding on orpines several times during the study and, even though not visually observed, there are most likely many other insects feeding on the plants (Elmquist 2011), with the beetle Apion sedi as well as species of sawflies (Tenthredo) documented using orpine as a plant resource (Elmquist 2011). A possible explanation for the observed behaviour might be that the females use visual and/or olfactory cues in their plant host selection to evaluate plant suitability based on their quality, or to avoid competition and/or predation (Refsnider & Janzen 2010). Some lepidopterans use visual and olfactory information to determine whether a plant is occupied by another species and disregard these plants to avoid competition (Sato et al. 1999, Refsnider & Janzen 2010). Damaged plants also attract predators and/or parasitoids, which has been shown to be repelling to some lepidopterans (Andersson & Alborn 1999, De Moraes, Mescher & Tumlinson 2001, Refsnider & Janzen 2010).

Exploitative competition between conspecifics could also affect offspring performance and survival, and in some cases even result in being consumed by conspecifics (Refsnider & Janzen 2010, Keplan & Denno 2007), whereas some species avoid plants that already have eggs

29 deposited on the leaves or stem (Sato et al. 1999). Based on previous surveys as well as this study, it seems that S. orion is not particularly discriminative of plants already inhabiting conspecific eggs with some plants having more than ten eggs on a single plant (Elmquist 2011, Carlsson & Elmquist 2009), nor have any cannibalistic behaviours been observed (Carlsson & Elmquist 2009). However, it is not known whether S. orion discriminates against plants inhabiting eggs and/or larvae from other species, this remains to be studied. Another possible explanation would be that females avoid damaged plants to minimize the risk of predation, as has been illustrated in other lepidopterans (Andersson & Alborn 1999, De Moraes, Mescher & Tumlinson 2001, Refsnider & Janzen 2010).

Since these aspects of plant avoidance/preference have not been studied properly for S. orion the discussions above are highly speculative. One should be careful making conclusions based on general predictions, as oviposition behaviour can vary greatly between and even within species. However, I present results that indicate that S. orion females have discriminative behaviour of plant hosts when it comes to oviposition; the reasons for this behaviour remain to be studied.

Direct effects Considering the direct effects, there was a substantial amount of eggs that disappeared during the season. Out of the 255 eggs that were lost, only sixteen eggs were lost due to roe deer foraging, the rest were eaten by other insects or, in most cases, lost for unknown reasons. Another element of this study is that many of the eggs did not develop into larvae later in the season; rarely more than 1-3 larvae were seen on a single plant. What happens to the remaining undeveloped and disappearing eggs is unknown (Elmquist 2011), but there can be multiple explanations. Apart from the eggs removed due to incidental feeding, there are most likely many eggs that were subjected to diseases and predation (Carlsson & Elmquist 2009, Elmquist 2011), while some eggs might have remained undeveloped or fallen off the plant. As discussed in the previous section, herbivory on some plants might induce production of volatiles (chemical compounds) as a defence mechanism to herbivory, and these chemical signals are used by some predators and parasitoids to find prey (Dicke & van Loon 2000, Awmack & Leather 2002). While this interaction has not been studied in S. orion, it can be a possible explanation. Some individual larvae have been observed, in previous inventories, moving between host plants (Carlsson & Elmquist 2009), but it is unlikely that this is a main reason for absence of larvae. Another possible explanation, but not yet studied, for the unsuccessful larva development is inbreeding depression. Small and isolated populations have a higher risk of experiencing inbreeding, which consequently can have negative effects on both egg, larvae and adult survival and performance (Saccheri et al. 1998). Since S. orion occurs in quite small and isolated subpopulations in Sweden (Elmquist 2011) there is also risk of inbreeding. However, the knowledge about the genetic structure and variation of the Swedish populations are too limited (Elmquist 2011) and needs to be further studied to be able to evaluate the effects of S. orion.

It would be interesting and potentially important for future studies to examine this evident loss of eggs and unsuccessful larva development. What is the reason behind this pattern? How many of the eggs develops into larva and what happens to the rest? Considering this substantial loss of eggs and unsuccessful larval development, one could argue that egg surveys of S. orion might give an inaccurate perception of the population size. While this is the current best method studying this species at this day (County administrative board of Stockholm 2019), there are good reasons to be careful when drawing conclusions based simply on these data.

Cage experiment I found a significant difference in total egg number between caged and control plants, with more eggs on plants that had experimental protection. This indicates that the second hypothesis stating ‘fewer eggs and/or larvae will be found on the plants that do not have any browsing protection’ can be accepted. However, these results might be biased since many of the cages, when established, already had eggs. This choice was made with the purpose to compare any eventual differences in butterfly abundance between protected and unprotected plants, hence browsing caused a significant loss of eggs outside the cages compared to inside the cages. Since there was no significant difference in egg survival between protected and control plants it is not clear whether these cages had the desired protective effect. Therefore, the hypothesis should remain unconfirmed. The roe deer browsing activity was altogether too limited to make any comparisons or statements about the effect of herbivory on protected and control plants. However, it seems that it had some protective effect since no roe deer damage was observed in the cages. Likewise, there was a significant difference in damage between protected and control plants, with more damage plants outside the cages. This observation is interesting since other insects could move freely in and out of the cages and not hindered by this experimental protection.

The cage experiment was successful in the sense that it kept roe deer out from the protected plants and did not seem to be an obstacle for S. orion movements. The fact that roe deer activity seems to be limited on this island this year is something that cannot be controlled, since deer population size and movements vary over time (Jarnemo et al. 2018). Alternatively, it also might be that roe deer do not pose a threat to the population. But since roe deer have been documented eating S. orion eggs and larvae in other localities (Carlsson & Elmquist 2009), it appears that they are a disturbing element even though the scale of disturbance is not known. For future studies, it would be interesting to repeat the cage experiment in another locality, preferably in mainland where the abundance of deer is generally higher. For instance, a potential site to retest this experiment would be in Södermanland county, where Carlsson & Elmquist (2009) have observed roe deer damage in several S. orion localities. One could test placing similarly constructed cages, or perhaps use some form of fence to enclose a smaller area, to evaluate what effect this have on the orpine plants and the S. orion egg-laying behaviour.

Since this experiment was conducted in an island, the access to this locality might be a limiting factor. Even though roe deer can move between islands (Jarnemo et al. 2018), it may still be difficult for the deer to access this locality. Hence, roe deer might pose a greater threat to population in other localities and this is worth investigating as a conservation effort for S. orion.

S. orion larvae The initial idea was to analyse direct and indirect effects of roe deer browsing on both eggs and larvae. However, I estimated the number of larvae being too small, with only 39 individuals in total, to compute any statistical tests. Therefore, I decided to exclude these data from any analysis.

Error sources A possible source of error in this study is the incapability of detecting what type of insect had caused the damage in many of the eaten plants. Since all plants were examined by visual observations there is an uncertainty regarding the source of damage. For instance, some plants had bite marks similar to the ones found on plants inhabiting the P. apollo larvae, but since no herbivores could be found on these plants they were treated as “unknown insect”. Hence, I did not have enough experience and knowledge to determine which bite marks were caused by

31 specific insect species. Since the object of this study was to detect roe deer damage, classifying in detail which insect was responsible for the unknown damage was not investigated further. However, this should be taken into consideration when examining the data. Conclusion The cage experiment was successful in keeping roe deer out from the protected plants, but the herbivory pressure of roe deer was too small this year to be able to evaluate its effects. However, while I was not able to demonstrate a significant effect of browsing on the S. orion population in Mörtö, there were other interesting results. I found selective oviposition behaviour based on plant properties and plant quality. It appears that other herbivores, roe deer and herbivorous insects, indirectly affects S. orion ovipositing behaviour by altering the availability of suitable plant hosts, with evidence of females avoiding plants that had been exposed to herbivory damage. The reason for this behaviour is proposed to be due to avoid competition and/or predation, which has been observed in other lepidopterans (Andersson & Alborn 1999, De Moraes, Mescher & Tumlinson 2001, Refsnider & Janzen 2010). However, this interaction has not been studied in detail in S. orion and needs to be further investigated. Another interesting observation was that a substantial number of S. orion eggs were lost during the season. Some eggs were lost due to browsing by roe deer, P. apollo and other herbivorous insects, whereas the reason for the main egg loss could not be identified. Some eggs were probably lost due to disease and predation (Carlsson & Elmquist 2009, Elmquist 2011), but this remains to be studied in more detail.

References Andersson P & Alborn H. 1999. Effects on oviposition behaviour and larval development of Spodoptera littoralis by herbivore-induced changes in cotton plants. Entomologia Experimentalis et Applicata 92: 45–51.

Awmack CS & Leather SR. 2002. HOST PLANT QUALITY AND FECUNDITY IN HERBIVOROUS INSECTS. Annu. Rev. Entomol 47:817–44.

Baines D, Sage RB & Baines MM. 1994. The Implications of Red Deer Grazing to Ground Vegetation and Invertebrate Communities of Scottish Native Pinewoods. Journal of Applied Ecology 31: 776-783.

Carlsson B & Elmqvist H. 2013. Rearing caterpillars of the Chequered Blue Butterfly, Scolitantides orion (Lepidoptera: Lycaenidae), on orpine and stonecrop. Entomologisk Tidskrift 134: 105–109.

Carlsson, B. & Elmquist, H. 2009. Inventering av Svartfläckig blåvinge Maculinea arion och Fetörtsblåvinge Scolitantides orion Södermanlands län 2005–2008. Rapport Nr 2008: 6. Länsstyrelsen Södermanlands län. Nyköping.

Chan Y & Walmsley RP. 1997. Learning and understanding the Kruskal-Wallis one-way analysis-of-variance-by-ranks test for differences among three or more independent groups. Physical therapy, 77: 1755–1762.

County Administrative Board of Stockholm. 2019. Utvärdering av inventeringsmetodik för fetörtsblåvinge Scolitantides orion – med förslag på en standardiserad inventeringsmetodik. Länsstyrelsen i Stockholms län, rapport 2015:12.

Cribari-Neto F & Zeileis A. 2010. Beta Regression in R. Journal of Statistical Software 34. www.jstatsoft.org/article/view/v034i02/v34i02.pdf.

De Moraes CM, Mescher MC & Tumlinson JH. 2001. Caterpillar-induced nocturnal plant volatiles repel conspecific females. NATURE 410: 577-580.

Côté SD, Rooney TP, Tremblay JP, Dussault C & Waller DM. 2004. Ecological impacts of deer overabundance. Annual Review of Ecology, Evolution, and Systematics 35: 113–47.

Dempster JP. 1997. The Role of Larval Food Resources and Adult Movement in the Population Dynamics of the Orange-Tip Butterfly (Anthocharis cardamines). Oecologia 111: 549-556.

Dicke M & van Loon JJA. 2000. Multitrophic effects of herbivore-induced plant volatiles in an evolutionary context. Entomologia Experimentalis et Applicata 97: 237–249.

Ehrlich PR & Raven PH. 1964. Butterfly and plants: A study in coevolution. Evolution 18: 586- 608.

Eliasson CU, Ryrholm N, Holmer M, Jilg K & Gärdenfors U. 2005. Nationalnyckeln till Sveriges flora och fauna. Fjärilar: Dagfjärilar. Hesperiidae – Nymphalidae. Artdatabanken, Slu, Uppsala.

Elmquist H. 2011. Åtgärdsprogram för fetörtsblåvinge 2011–2015 (Scolitantides orion). Naturvårdsverket, Rapport 6424.

Elmqvist H & Carlsson B. 2009. Inventering av Svartfläckig blåvinge Maculinea arion och Fetörtsblåvinge Scolitantides orion Södermanlands län 2005–2008. Rapport Nr 2008: 6. Länsstyrelsen Södermanlands län. Nyköping.

Jarnemo A, Neumann W, Ericsson G, Kjellander P & Andrén H. 2018. Hjortvilt i Sverige - En kunskapssammanställning. NATURVÅRDSVERKET RAPPORT 6819, Bromma.

Garner SM, Hartley SE, Davies A & Palmer SCF. 1996. Carabid communities on heather moorlands in northeast Scotland: The consequences of grazing pressure for community diversity. Biological Conservation 81: 275-286.

Gauthier TD. 2001. Detecting Trends Using Spearman’s Rank Correlation Coefficient. Environmental Forensics 2: 359-362.

Gómez JM & González-Megías A. 2002. ASYMMETRICAL INTERACTIONS BETWEEN UNGULATES AND PHYTOPHAGOUS INSECTS: BEING DIFFERENT MATTERS. Ecology 83: 203–211.

Gómez J & González-Megías A. 2007. Long-term effects of ungulates on phytophagous insects Ecological Entomology 32: 229–234

Gothnier M & Jaramillo M. 2019. Fetörtsblåvinge återfunnen i Stockholms skärgård – Fauna och Flora 114(3): 2–6.

Haddad NM, Tilman D, Haarstad J, Ritchie M & Knops JMH. 2001. Contrasting Effects of Plant Richness and Composition on Insect Communities: A Field Experiment. American Naturalist 158: 17-35.

Hegland SJ, Lilleeng MS & Moe SR. 2013. Old-growth forest floor richness increases with red deer herbivory intensity. Forest Ecology and Management 310: 267–274.

Hegland SJ & Rydgren K. 2016. Eaten but not always beaten: winners and losers along a red deer herbivory gradient in boreal forest. Journal of Vegetation Science 27: 111–122.

Horsley SB, Stout SL & Decalesta DS. 2003. White-tailed Deer impact on the vegetation dynamic of a northern hardwood forest. Ecological Applications 13: 98-118

Jiang J. 2007. Linear and Generalized Linear Mixed Models and Their Applications. 1st Ed. Springer-Verlag, New York.

Lessard JP, Raynolds WN, Bunn WA, Genung MA, Cregger MA, Felker-Quinn E, Barrios- Harcia MN, Stevenson ML, Lawton RM Brown CB, Patrick M, Rock JH, Jenkins MA, Bailey JK, Schweitzer JA. 2012. Equivalence in the strength of deer herbivory on above and below ground communities. Basic and Applied Ecology 13: 59–66.

Kaplan I & Denno RF. 2007. Interspecific interactions in phytophagous insects revisited: a quantitative assessment of competition theory. Ecology Letters 10: 977–994.

Knight TM, Caswell H & Kalisz S. 2009. Population growth rate of a common understory herb decreases non-linearly across a gradient of deer herbivory. Forest Ecology and Management 257: 1095–1103.

Kruess A & Tscharntke T. 2002. Grazing Intensity and the Diversity of Grasshoppers, Butterflies, and Trap-Nesting Bees and Wasps. Conservation Biology 16:1570–158.

Källgård A. 2013. Sveriges öar. 3rd ed. Carlsson Bokförlag, Stockholm.

Marttila O, Saarinen K & Marttila P. 2000. Six years from passing bell to recovery: Habitat restoration of the threatened Chequered Blue Butterfly (Scolitantides orion) in SE Finland. Entomologica Fennica 11: 113–117.

McShea WJ & Rappole JH. 1992. White-tailed deer as Keystone Species within Forest Habitats of Virginia. Virginia Journal of Science 43: 177–186.

Mossberg B & Stenberg L. 2007. Svensk fältflora. 3rd edition. Wahlström & Widstrand, Värnamo.

Murrar S & Brauer M. 2018. Mixed Model Analysis of Variance. In: Frey BB (eds.). The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation, pp. 1075-1078. SAGE Publications, Inc., Thousand Oaks.

Naiman RJ. 1988. Animal Influences on Ecosystem Dynamics. Bioscience 38: 750-752.

Nishizawa K, Tatsumi S, Kitagawa R & Mori AS. 2016. Deer herbivory affects the functional diversity of forest floor plants via changes in competition-mediated assembly rules. Ecological Restoration 31: 569–578.

Pollard E & Cooke AS. 1994. Impact of Muntjac Deer Muntiacus reevesi on Egglaying Sites of the White Admiral Butterfly Ladoga camilla in a Cambridgeshire Wood. Biological Conservation 70: 189-191

Prince PW. 2002. Resource-driven terrestrial interaction webs. Ecological Research 17: 241– 247.

Puentes A & Johnson MTJ. 2016. Tolerance to deer herbivory and resistance to insect herbivores in the common evening primrose (Oenothera biennis). Journal of Evolutionary Biology 29: 86-97

QGIS Development Team. 2019. QGIS Geographic Information System. Open Source Geospatial Foundation Project, version 3.6.0-Noosa with GRASS 7.6.0.

Refsnider JM & Janzen FJ. 2010. Putting Eggs in One Basket: Ecological and Evolutionary Hypotheses for Variation in Oviposition-Site Choice. The Annual Review of Ecology, Evolution, and Systematics 41:39–57.

Ricklefs R & Relyea R. 2014. Ecology: The Economy of Nature. 7th edition. W.H. Freeman, New York.

Robertson SM & Baltosser WH. 2018. Deer Browsing Influences Population Dynamics of the Ozark Baltimore Checkerspot (Euphydryas Phaeton Ozarkae: Nymphalidae). Journal of the Lepidopterists’ Society 72: 270-275.

Roberson EJ, Chips MJ, Carson WP and Rooney TP. 2016. Deer herbivory reduces web- building spider abundance by simplifying forest vegetation structure. PeerJ 4:e2538, DOI10.7717/peerj.2538.

Rooney TP & Waller DM. 2003. Direct and indirect effects of white-tailed deer in forest ecosystems. Forest Ecology and Management 181: 165–176.

Rooney TP. 2001. Deer impacts on forest ecosystems: A North American perspective. Forestry 74: 201–208.

Russell FL, Zippin DB & Fowler NL. 2001. Effects of White-tailed Deer (Odocoileus virginianus) on Plants, Plant Populations and Communities: A Review. The American Midland Naturalist 146: 1-26

RStudio Team. 2020. RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA. URL: http://www.rstudio.com/.

Saccheri I, Kuussaari M, Kankare M, Vikman P, Fortelius W & Hanski I. 1998. Inbreeding and extinction in a butterfly metapopulation. Nature 392: 491–494.

Sato Y, Yano S, Takabayashi J, Ohsaki N. 1999. Pieris rapae (Lepidoptera: Pieridae) females avoid oviposition on Rorippa indica infested by conspecific larvae. Appl. Entomol. Zool. 34: 333-337.

Seimann E. 1998. EXPERIMENTAL TESTS OF EFFECTS OF PLANT PRODUCTIVITY AND DIVERSITY ON GRASSLAND ARTHROPOD DIVERSITY. Ecology 79: 2057–2070

SLU Artdatabanken. 2020. Rödlistade arter i Sverige 2020. SLU, Uppsala

Smithson M, Verkuilen J. 2006. A Better Lemon Squeezer? Maximum-Likelihood Regression with Beta-Distributed Dependent Variables. Psychological Methods, 11: 54–71.

Suominen O, Danell K & Bergström R. 1999. Moose, Trees, and Ground-Living Invertebrates: Indirect Interactions in Swedish Pine Forests. Oikos 84: 215-226

Takagi S & Miyashita T. 2014. Scale and system dependencies of indirect effects of large herbivores on phytophagous insects: a meta-analysis. Population Ecology 56:435–445

Tränker A & Nuss M. 2005. Risk spreading in the voltinism of Scolitantides orion orion (Pallas, 1771) (Lycaenidae). Nota Lepidopterologica 28: 55–64.

Urbanek RE, Nielsen CK, Glowacki GA & Preuss TS. 2012. White-Tailed Deer (Odocoileus virginianus Zimm.) Herbivory in Herbaceous Plant Communities in Northeastern Illinois. Natural Areas Journal 32:6–14