The Role of Wood Ants (Formica Rufa Group) in the Arctic Tundra and How Climate Change May Alter This Role

Home , Formica, Formica aquilonia, Formica cinerea, Formica exsecta, Formica lugubris, Formica polyctena

The role of wood ants (Formica rufa group) in the Arctic tundra and how climate change may alter this role

Michael Meijer Master’s Degree Thesis in Biology 60 ECTS

Department of Ecology and Environmental Science, Umeå University, SE- 901 87 Umeå, Sweden

Supervisor: Micael Jonsson Front cover: Michael Meijer, Abisko, Sweden.

Cite as: Meijer, M. 2020. The role of wood ants (Formica rufa group) in the Arctic tundra and how climate change will alter this role. M.Sc. Thesis. Department of Ecology and Environmental Science, Umeå University, Sweden

Abstract In the Arctic tundra, wood ants play an important ecological role in aerating the soil, cycling nutrients, for seed dispersal and, as biological control by preying on forest pest insects during outbreaks. The increase in temperature, caused by climate change, is positively associated with ant abundance. This could accelerate the wood ants’ effects on the ecosystem, with potentially dramatic consequences for associated taxa. It is, however, still unclear to what extent the ants influence the vegetation and arthropod community. The aim of this study is to investigate the effects ants have on the Arctic tundra ecosystem and how climate change may modify these effects. The study was conducted in Abisko national park, north Sweden, were two study sites were selected: one at low altitude and one at high altitude. I found that wood ants had a substantial effect on the vegetation community close to the mound, with a positive effect on different kind of vascular plant species, and a negative effect on rushes, mosses, and lichens. All the arthropods taxonomic orders and most of the families were positively affected by the presence of ant mounds. Ant mound abundance and volume were positively related with annual insolation and GPP, which indicates that climate change will increase ant abundance in the Arctic tundra. Thus, my results suggest that future climate change will have significant effects on Arctic tundra vegetation and arthropod communities, via positive effects on ant abundance.

Table of content Abstract ...... 3 1 Introduction ...... 1 1.1 Wood ant interaction...... 1 1.2 Objective ...... 2 2 Method ...... 3 2.1 Study area ...... 3 2.2 UAV data ...... 3 2.3 Ant nest abundance ...... 4 2.4 Arthropods communities ...... 4 2.5 Vegetation communities ...... 4 2.6 Data analyses ...... 5 3 Results ...... 8 3.1 Ant mound inventory UAV ...... 8 3.2 NMDS arthropod community ...... 10 3.3 Influence of ant mounds on vegetation ...... 12 3.3.1 Vegetation on top of ant mounds ...... 13 3.4 GLM models ant mound abundance and volume ...... 15 4 Discussion ...... 17 4.1 Influence of wood ants on the vegetation ...... 17 4.2 Wood ants’ interaction with arthropod community ...... 17 4.3 Influence of climate change on the wood ants ...... 18 4.4 Vegetation on the mound ...... 19 4.5 Methodology ...... 19 4.6 Conclusion ...... 19 Acknowledgement ...... 21 References ...... 22 Appendix I: Study sites Appendix II: Equations for distance to ant mound to vegetation Appendix III: Output from SIMPER Appendix IV: Average values nest material between groups Appendix V: Fixed vectors for nest material Appendix VI: Invertebrate abundance and biomass for both sites

1. Introduction Ants (Formicidae) are important ecosystem engineers that play key roles in shaping the community structure in terrestrial ecosystems (Jones et al., 1994; Folgarait, 1998). Many studies have investigated the local diversity and abundance of ants and their effect on the ecosystem in different types of habitats all over the world (Fowler & Claver, 1991; Brener & Ruggiero, 1994; Samson et al., 1997). However, one biome where effects of ant populations are not so well studied, compared to the other biomes, is the Arctic tundra. The Formicidae species in the Arctic tundra consist of a small number of species, compared to other biomes, and thus has not received a lot of attention by researchers (Gregg, 1972; Francœur, 1983; Nielsen, 1987). Nevertheless, in the Arctic , ants occur locally in high densities, suggesting that they play an important ecosystem role in aerating the soil, cycling nutrients, as biological control agent by preying on forest insect pests, and for seed dispersal (Nielsen, 1987; Folgarait, 1998; Jones et al., 1994; Kovář et al., 2013).

One of the most abundant ant species that colonize the northern hemisphere is the wood ant (Formica rufa group). Wood ants build large and long-lasting nest mounds, that can stay in place for decades. Constructing and maintaining the nest and colony cost a considerable amount of energy and material. During the construction, ants modify the soil by turning and aerating the soil, allowing oxygen and water to be absorbed by plant roots (Denning et al., 1977; Gotwald, 1986; Majer et al., 1987; Cherrett, 1989). By foraging on food and plant material and moving it to a central location, they do not only influence the availability of nutrients in the ant mound, but also in the surrounding (Petal, 1978; Mandel & Sorenson, 1982; Ohashi et al., 2007; Kilpeläinen et al., 2007). For example, nutrients that are released from the organic material of the nest remain near the ant mound, creating nutrient rich hot spots that benefit certain plant species (Lenoir et al., 2001). Also, old ant mounds that are abandoned contribute to ecosystem heterogeneity (Carlson & Whitfold, 1991).

1.1 Wood ant interaction Ants can have a strong influence on the composition of entire arthropod communities. Wood ants will prey on different arthropods depending on how available they are as potential prey (Horstmann, 1972). Wood ants’ colonies also change arthropod communities by excluding competing predators, such as spiders, harvestmen, and beetles (Skinner, 1980; Brüning, 1991; Halaj et al., 1997; Hawes et al., 2013; Reznikova & Dorosheva, 2004). Further, aphids and lepidoptera caterpillars can have mutualistic relationships with ants (Dolling, 1991; Auclair, 1963; Resh, 2009). In exchange of honeydew the ants protect aphids and lepidopterans against predators, and this relationship is called trophobiosis (Hölldobler & Wilson, 1990; Fielder, 1991). In Europe, lepidopterans that have a trophobiosis relationship with ants belong to the family of Lycaenidae, with around 75% of the species of this family exhibiting a positive relationship with ants (Eliot, 1973).

On the other hand, there are also many arthropods specialized in exploiting recourses of ant colonies and that benefit from ant activity. This relationship is called myrmecophily (Oliver et al., 2008). Some myrmecophilic species can disguise themselves and enter the nest unnoticed, and thereby they can then use the food resources and benefit from the indirect protection of the colony (Kronauer & Pierce, 2011). Integration into the colony is either through producing soothing agents, chemical camouflage, or chemical mimicry (Akino, 2008). More than 100 myrmecophilic species that can be found in wood ant nests have been identified so far (Stockan, 2016). In addition to chemical myrmecophily, many species of spiders, true bugs, and short-winged beetles are myrmecomorphilic, meaning that they have a similar appearance as ants (Huang, 2011; Lapeva-Gjonova, 2013). This similarity in appearance makes it possible for certain species to approach ants more easily to catch them. Several beetles follow wood ant streets to steal and eat the prey caught by the ants (Dettner, 1994). Many species that are not considered myrmecophiles or myrmecomorphilic, such as the springtail Folsomia quadrioculata, and earthworm species, such as Dendrodrilus

1 rubidus, still have higher abundance in the nest than in the surrounding soil (Laakso & Setälä, 2000; Laakso & Setälä, 1997). These species benefit from the ant activity or protection, but are not depended on them.

Wood ants can also have mutualistic relations with plants, called myrmececochory, were they disperse the seeds in exchange for elaisome, a detachable lipid-rich appendage on the seeds (Andersen, 1988). The ants extract the elaiosome from the seed once it has been carried to the nest (Berg, 1958; Berg, 1975; Culver & Beattie, 1978; Beattie, 1985). As a result, the seeds are safely transported to a nutrient-rich substrate where they also are protected from seed predators (Bond, 1985; Beattie, 1985). Wood ant foraging for seeds can have a large impact on the ecosystem. In a study by Wardle et al. (2011), long-term wood ant exclusion increased the abundance of herbaceous plants, indicating that the ants normally forage a significant proportion of their seeds. The same study also found that wood ant exclusion altered the soil microbial community with consequences for nutrient and carbon cycling (Wardle et al., 2011).

Ant predation on insect herbivores can also influence the plant communities (Skinner & Whittaker, 1981; Risch & Carroll, 1982; Fowler & MacGarvin, 1985; Perfecto & Sediles, 1992; Mahdi & Whittaker, 1993; Schmitz et al., 2000; Speight et al., 2008). For example, herbivore damage was significantly lower and reproductive success (proportion of flowers succeeding to berries) was significantly higher in Vaccinium myrtillus near ant mounds (Atlegrim, 2005). Ants can also increase the damage that aphid colonies are causing on plant and trees by protecting the colony from predators and diseases (Adlung, 2009; Kilpeläinen et al., 2009). Wood ants can act as biocontrol agents by preying on forest insect pests, such as spruce bud worm, pine moth and Colorado beetle (Young & Campbell, 1984; Godzińska, 1986; Kim & murakami, 1983). Thus, during outbreaks of insect pests, green islands of trees can often be found around ant mounds (Laine & Niemelä, 1980).

1.2 Objective Earlier studies have shown that ant abundance is influenced by mean temperature, primary productivity, and seasonality (Kaspari et al., 2000). Due to climate change, the mean annual temperature and the growing season have increased significantly in the Arctic tundra and will increase even more in the future (IPCC 2007; Soja et al., 2007). Mean annual surface air temperatures in the Arctic have risen by 2.5 °C since 1900 (Przybylak, 2007; ACIA, 2004). This warming trend will continue, and some models predict that temperatures in the Arctic tundra will increase by 2.4-7.8 °C by the end of the 21st century compared to during 1980- 1999 (IPCC, 2007). An increase in temperature is strongly positively associated with ant abundance, foraging activities, and other behaviours (Kaspari et al., 2000; Sanders et al., 2007), and could therefore also accelerate wood ant effects on nutrient cycles and organic matter decomposition, with potentially dramatic consequences for associated taxa and the entire ecosystem (Lensing & Wise 2006; Moya-Laranõ & Wise 2007; Crist, 2000). Ants may have a lower diversity of species in the Arctic tundra compared to the tropics, but effects of an increased temperature will likely be more dramatic in the Arctic tundra (Flato & Boer, 2001; IPCC 2007; Holland & Bitz, 2003; Flato, 2004; Hu et al., 2004).

The overall objective of this study was to investigate how much influence ants have on the Arctic tundra ecosystem and how climate change will modify this influence. This study looked in detail at how ant mounds influence the surrounding vegetation and arthropod community. Also, I investigated which environmental variables influenced the ant mound abundance and volume on the local scale in the Arctic tundra, especially what the effect of terrestrial gross primary productivity (GPP) was. In this study NDVI is used as a proxy for GPP. To do this, I tested the following hypotheses:

1. Ant mound abundance influence and change the arthropod community abundance and structure.

2. Ant mounds influence vegetation structure with distances from the ant mounds and this influence differs among vegetation types. 3. Ant mound abundance and volume in the Arctic tundra increases with increasing terrestrial GPP. 2. Method 2.1 Study area The study was conducted in Abisko national park (68°19’23’’N, 18°51’57’’E), northern Sweden. The study area consists out two sites, one low-altitude site (LA) with an average attitude of 530 meter and one high-altitude site (HA) with an average attitude of 699 meter. Both sites have an area of approximately 21 hectares (LA: 20.4 ha; HA: 21.1 ha). The distance between the areas is 2.2 km. Both study areas are representative of the Arctic tundra ecosystem, but the high-altitude area have, on average, 1.2 °C lower temperature (based on that the mean temperature changes gradually with 0.7 °C with every 100 m change in latitude). Field data was collected in the summer of 2019 (). The dominant vascular plant species are Betula nana and Salix spp., and other abundant vascular plant species, such as Eriophorum spp., Carex spp., Cassiope tetragona, Vaccinium vitis‐idaea, Vaccinium uliginosum, Empetrum nigrum, mosses, and lichens (Appendix I).

2.2 UAV data Remote sensing with unmanned aerial vehicles (UAV) was used to determine the ant mound abundance in both study sites. With the UAV, four different raster maps were obtained, including RGB (seq.), RGB (g9x), NDVI in four defined spectral bands: green (G) 550/40 nm, red (R) 660/40 nm, red-edge (RE) 735/10 nm, and near-infrared (NIR) 790/40 nm, as well as a combustion map of different multispectral raster bands (green, red, and nir). An eBee fixed-wing drone was used (senseFly, Switzerland), in combination with a Sequoia multi-spectral sensor (Parrot, France) to scan the area. This sensor provides multispectral imagery in four spectral bands (green, red, red edge, and nir) and complementary RGB imagery. For photogrammetric processing Pix4Dmapper (Pix4D, Switzerland) was used. All UAV imagery was georeferenced using 4-7 DGPS measured positioning points within each core area marked by crosses. This resulted in an approximate location accuracy of ~0-40 cm for specific locations in the core areas, with a clear tendency to the lower, more precise range. There are three sensors involved: Multi-spectral Parrot sequoia flown at an elevation of 106 m providing a ground resolution of ~12 cm after image processing, RGB Canon G9X at 98 m elevation (~2.3 cm), and RGB Sony WX at 98 elevation (~2.8 cm). The areas were scanned in June-July 2017. In the four raster maps, the ant mounds were marked to estimate ant mound abundance, and to estimate which raster map had the highest accuracy. Ground survey plots were made to calculate the true positive and false positive accuracy. The maps were scanned in rows starting from the top right corner in a straight line down on a scale of 1:250.

A Digital Elevation Model (DEM) was created (©Lantmäteriet; 2 m spatial resolution) from a corresponding lidar (©Lantmäteriet; at 0.25 points/m2) based surface height model, generated at 1 m spatial resolution. The DEM was used to generate several environmental datasets that can be used as predictors using SAGA GIS Version 7.4.0 (Conrad et al., 2015). These include Topographic Wetness Index (Boehner & Conrad, 2001), Wind Exposition Index (Boehner & Conrad, 2015), vegetation height, Annual Course of Daily Insolation (Conrad, 2012a), and Terrain Convexity (Conrad, 2012b). In addition, a relative height map was used to obtain data about the altitude. Both the lidar (Lantmäteriet:Laserdata 2018 LAZ) and the relative height map (Lantmäteriet:Höjddata 2-m raster) were obtained on: https://zeus.slu.se/. Furthermore, a vegetation cover classification map was created in Rstudio with the randomForest package version 4.6-14 (Liaw, 2018) to collect data about the spatial location of different vegetation types in both study sites.

2.3 Ant nest abundance Nine ground survey plots were created in the LA site and six in the HA site, to test the sensitivity of each raster map (Figure 1). The plots were 20 by 20 meters and were selectively chosen to represent different vegetation types (barren, marshland, grassland, and forest). When the ant mounds were marked, I also noted the height (cm), diameter at the bottom on the long and short side (cm), angle of the ant hill (°), vegetation cover (%), orientation of the hill (0° = North, 180° = South). Five ants were collected from each ant mound and preserved in a solution of glycol and water (ratio 1:1) to later determine the ant species (Bolton, 1994). The GPS points of the outline of the ground plots and the ant mounds were collected with a dGPS, with an accuracy of at least 10 cm. Mound volume was calculated from diameter, slope and height measurements, assuming a truncated cone shape.

2.4 Arthropods communities Two invertebrate pitfall traps were randomly placed at 33 locations in both study sites (Figure 1). The traps contained a solution of glycol and water (ratio 1:1), with a few drops of detergent to break the surface tension. The invertebrate traps were placed in the LA and HA site on 7-8 August 2019 and were retrieved after 6 days. Samples were cleaned and preserved in ethanol (70%). The captured arthropods were classified according to their taxonomic family. Samples were then dried in an oven for 48 h at 60 °C, and then weighed, to the nearest 0.0001 g, on a Mettler analytical balance.

2.5 Vegetation communities Five plots along a 4-meter gradient were laid out, with 1-meter space between the plots, from the boundary of the ant mound to study the effect of ants on the vegetation phenology and structure (Figure 1). Five active ant mounds were randomly selected in every ground plot for vegetation-gradient measurements. All plots were sampled during the peak-vegetation period in the summer of 2019, i.e. between August 1 and 15.

Sampling was done with a 10-point frame method. A 10-point frame was vertically placed by every plot, with 10 cm between every pin. At each contact with the pin the plant taxon was recorded. Vascular plants were identified to species (with the exception of Salix spp.), while other taxa were grouped as mosses, moss sphagnums, lichens, grass, sedges, or rushes, because of the difficulty of identifying some plants to species. The vegetation cover on the ant nest was estimated by using a quadrat techniques instead of point frame.

In addition to vegetation plots, I also collected nest material from the outer wall of the ant mounds. The nest material was then sorted into categories of branches, vole droppings, Andromeda polifolia material, Betula nana leaves, Empetrum nigrum, graminoids, other leaves, lichen, moss, rocks, and left-over material. Left-over material existed of soils and material that was too small to sort with tweezers. After sorting, the nest materials were dried in an oven for 24 h at 60 °C and then weighed. The weight of the different categories was divided by the total weight of the sample to estimate how much they contributed to the ant mound composition.

Figure 1. A schematic overview of the methodology used for this study.

2.6 Data analyses Data were analysed using Rstudio version 3.3.0 (R Core Team, 2019) and ArcMap version 10.5.1 (ESRI, 2016), and all figures were visualized with ggplot2 version 3.1.0 (Wickham, 2016). For every raster map, RGB (seq.), RGB (g9x), NDVI, MSP (seq., r, g, nir), I estimated the percentage Detection Accuracy (DA) and False Alarm Rate (FAR) to evaluate the maps (eq. 1a,b), using the data from the ground survey plots.

퐷퐴 = 푡푟푢푒 푝표푠𝑖푡𝑖푣푒 푛푢푚푏푒푟푠/푡표푡푎푙 푝표푠𝑖푡𝑖푣푒 푛푢푚푏푒푟푠 × 100% (푒푞. 1푎)

푓푎푙푠푒 푝표푠𝑖푡𝑖푣푒 푛푢푚푏푒푟푠 퐹퐴푅 = × 100% (푒푞. 1푏) 푡푟푢푒 푝표푠𝑖푡𝑖푣푒 푛푢푚푏푒푟푠 + 푓푎푙푠푒 푝표푠𝑖푡𝑖푣푒 푛푢푚푏푒푟푠

The highest accuracy map was selected based on the highest Positive Predictive Value (PPV)(eq.2). 푇푟푢푒 푝표푠𝑖푡𝑖푣푒 푃푃푉 = (푒푞. 2) 푇푟푢푒 푝표푠𝑖푡𝑖푣푒 + 푓푎푙푠푒 푝표푠𝑖푡𝑖푣푒

A regression model was built to understand the influence of ant mound volume and the surrounding environmental characteristics (NDVI, vegetation type, vegetation height, within the first meter) on the detection rate of the raster map with the highest accuracy (model 1).

Model 1 = Accuracy ~ Mound volume + NDVI + Veg. type + Veg. height

Influence of ants on vegetation Distance from the nest was treated as an environmental gradient to model the influence of ant mound presence on the surrounding vegetation. The package BasicTrendline version 2.0.3 (Weiping & Guangchuang, 2018) was used to estimate the best-fitted linear or non- linear relationship between distance from the nest and cover of every plant species/group (Model 2). The vegetation type was determined around the ant mounds to see how wood ants influence vegetation composition in different habitats. I used the vegetation cover classification raster map to divide the ant mounds into three vegetation types: grasses/low shrubs, marshlands, and forest.

Model 2 = 푃푙푎푛푡 푠푝푒푐𝑖푒푠/푔푟표푢푝 ~ 퐷𝑖푠푡푎푛푐푒 푎푛푡 푚표푢푛푑

Vegetation on the ant mounds was summarized in a non-metric multidimensional scaling (NMDS) analysis with the fitted vectors, calculated with the envfit function, to examine the relationship between vegetation composition on the ant mounds and the nest material composition from the mound. Only the plant species/groups and nest materials that varied significantly with mound size, or among ant species or vegetation type were used in the NMDS. The significance of vector fits was determined using 999 permutation tests. An analysis of similarity with the ANOSIM function, with Bray-Curtis index, was done to see if the nest material and vegetation on the nest were different with mound size, or among ant species or vegetation type. To investigate a potential effect of mound volume, I divided the nest into three categories: small (<0.02 m3), medium (0.02-0.04 m3) and large nests (>0.04 m3). A Similarity percentage analysis (SIMPER) was performed when there was a significant difference found with the ANOSIM function, to see which plant species and/or nest material contributed to at least to 70% of the differences. The NMDS, ANOSIM, and SIMPER were all performed with the vegan package version 2.5.6 (Oksanen, 2019).

Influence of ants on arthropods Density of ant mounds around the invertebrate traps was calculated, by using the most accurate raster map, to analyse the effect of ant mounds on the arthropod community. Density of mounds is expressed as the average distance of the 5 nearest ant mounds to an invertebrate trap. Then, arthropod data were subjected to NMDS in order to find patterns for effect of ant mounds. I fitted linear trends (fitted vectors) for the relationship between ant mound density and all the ground arthropod groups (i.e. Araneae, Coleoptera, Orthoptera, Opiliones, Hemiptera) and Mollusca onto the ordination, and assessed their significance with 999 permutations test. Smoothed surfaces were fitted onto the ordination with the ordisurf function (vegan package), as there may be a non-linear relationship between these variables and the ordination space. Generalized additive models (GAM) with thin plate splines (Wood, 2003) were used to fit smoothed surfaces onto the ordination. NMDSs performed separately for taxonomic orders and families.

Environmental factors influencing ant abundance and volume Generalized Linear Models (GLM) were built to see which environmental variables had a significant influence on ant mound abundance and volume. Environmental variables that were used as predicators were wetness index, NDVI, altitude of mound, vegetation height index, annual solar insolation, slope of the terrain, distance to nearest tree, and wind exposing index (Model 3).

푀표푑푒푙 3 = 퐴푏푢푛푑푎푛푐푒/푣표푙푢푚푒~푊푒푡푛푒푠푠 + 푁퐷푉퐼 + 퐴푡푡𝑖푡푢푑푒 + 푉푒푔. 퐻푒𝑖푔ℎ푡 + 𝑖푛푠표푙푎푡𝑖표푛 + 푆푙표푝푒 + 푇푟푒푒 + 푤𝑖푛푑

Distance to nearest tree was derived from the vegetation cover classification dataset. The average value of each environmental variable was extracted from within a 3-meter radius around every ant mound measured in the survey ground plots, to investigate the effect on ant mound volume. A 3-meter buffer was chosen so the spatial scale at which I extracted environmental data was greater than the presumed spatial effects of ants on the environment. For ant abundance, a GLM with Poisson logit (link) was used. Environment data for the ant abundance model was obtained by extracting data from 500 random generated plots with a radius of 3 meter in the LA site. The raster map with the highest accuracy was used to obtain the ant mound abundance in the random plots. Akaike Information Criterion (AIC) together with stepwise variable selection were used to select the final model.

A dominance analysis with the RELAIMPO package version 2.2.3 (Ulrike, 2016) was done to understand the relative importance of predicators on ant mound abundance and volume. The dominance analysis in RELAIMPO is only designed for GLM with Bernoulli and Gaussian family, and not Poisson like the ant abundance model. It should be reasonable to use this method also for a Poisson model to obtain a rank order of predictor contribution on the GLM, given that the Poisson and Logit model in GLM only differ in their link function and probability distribution (Tonidandel & LeBreton, 2010; Azen & Traxel, 2009).

3. Results In total, 56 mounds were found in the nine plots on the LA site, where 18 mounds belonged to Formica uralensis, 24 to Formica exsecta, and 12 to Formica lugubris. Two mounds were inactive, and thus the species could not be determined. On the HA site, 5 mounds were found, and they all belonged to Formica uralensis.

3.1 Ant mound inventory UAV The average false alarm rate and detection accuracy in the LA site were 20% and 66.1%, respectively, for the RGB (seq.) raster map, 22.8% and 78.86%, respectively, for the NDVI map, 7.5% and 66.1%, respectively, for the MSP (seq., r, g, nir) map, and 0% and 69.7%, respectively, for the RGB (g9x) map (Figure 2; Table 1). There were no nests found in plot 3 in the LA site (Table 1). In total, there were 6 ant mounds found in the ground survey that were not detected by any of the raster maps. None of the 5 ant mounds found on the HA site were spotted with any of the raster maps. The RGB (G9x) had the highest positive predictive value (PPV=1), followed by MSP (Seq., r, g, nir) (PVV=0.926), RGB (Seq.) map (PVV= 0.804), and NDVI (PVV= 0.785).

Table 1. The false alarm rate and detection accuracy of every raster map per plot in the LA site. Plot 1 Plot 2 Plot 3 Plot 4 Plot 5 Plot 6 Plot 7 Plot 8 Plot 9 Average False alarm rate

RGB (Seq.) 37.50% 33.30% 0.00% 0.00% 33.00% 12.50% 0.00% 12.50% 25.00% 20.00% NDVI 20.00% 0.00% 0.00% 0.00% 22.20% 20.00% 0.00% 14.20% 58.30% 22.80% MSP (Seq., r,g,nir) 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 16.70% 22.20% 7.50%

RGB (G9x) 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Detection accuracy RGB (Seq.) 80.00% 66.70% 100.00% 50.00% 55.60% 66.70% 90.00% 85.80% 33.40% 66.10% NDVI 80.00% 66.70% 100.00% 75.00% 88.90% 88.90% 80.00% 85.80% 55.60% 78.86% MSP (Seq., r,g,nir) 80.00% 0.00% 100.00% 75.00% 55.60% 77.80% 80.00% 71.60% 44.50% 66.10% RGB (G9x) 100.00% 66.70% 100.00% 50.00% 55.60% 77.80% 80.00% 71.60% 55.60% 69.70%

A GLM with logic function was built to investigate variables that influence detection probability on the RGB (G9x) map. Here, the only significant predictor variables were volume of the ant mound (p<0.001, β = 0.010) and NDVI (p=0.037, β=-0.223). Vegetation height (p=0.078), and vegetation type (p=0.591) were not significant. The probability of detecting a small ant mound (<0.02 m3, n=17) in the RGB (G9x) map was 41.17%, a medium ant mound (o.02-0.04 m3, n=17) was 67.7%, and a large ant mound (>0.04 m3, n=22) was 95.45%. The average mound volume was 0.037 ± 0.030 m3 in the LA and 0.012 ± 0.009 m3 on the HA site.

Figure 2. Four different raster maps of the LA site that show the ground survey plots and the marked ant mounds. (A) NDVI map with 221 inventoried mounds, (B) MSP (seq., r.g,nir) map with 281 inventoried ant mounds, (C) RGB (seq.) with 212 inventoried ant mounds and (D) RGB (g9x) map with 272 inventoried ant mounds.

3.2 NMDS arthropod community The RGB (G9x) map was used to calculate the distance of the ant mounds in relation to the invertebrate traps (x ̅ = 30.62 ± 16.04 m). Ant mound distance as a fitted vector was significantly (P < 0.001) correlated in both the abundance (Figure 3a) and biomass (Figure 3b) ordination plot. All the orders were present in the lower value gradients in both NMDS ordination plots (Figure 3). Observing the gradients lines in the NMDS plots, it can be noticed that the abundance and biomass fitted surface is strongly non-linear.

Figure 3. Non-metric multidimensional scaling (NMDS) ordination plot (based on a Bray-Curtis similarity matrix) of (A) arthropod abundance on ant mound distance density (MD) (Stress = 0.181) and (B) biomass on ant mounds (Stress =0.18), with fitted vectors and smoothed surfaces. The arrow points to the direction of most rapid change in the variable, while the length of the arrow is proportional to the correlation between ordination and variable. The significance of vector fits was determined using permutation tests (n = 999) at the p = 0.05 level. The blue contour lines correspond to density ant mounds’, fitted with GAM, P<0.001 for both abundance and biomass.

Taxonomic families were subjected to an ordination analyse if there were multiple families found within the same orde. This only applies to Coleoptera and Aranea. Within the orders Orthoptera and Opiliones, only the families Acrididae and Phalangiidae, respectively, were found in the invertebrate traps. Ant mound distance as a fitted vector was significantly (P < 0.001) correlated in both the abundance (Figure 4a) and biomass (Figure 4b) ordination plot. Most families occurred in the lower value gradients in both NMDS ordination plots, except for Staphylinidae, Linyphiiae, Zoridae, and Elateridae. Looking at the location of the families gives the impression that families from the same taxonomic order are similarly affected by ant mound density. For example, Curculionidae, Chrysomelidae, and Carbabidae (Coleoptera) group together, as do Thomisidae, Thomisidae, and Gnaphosidae (Araneae).

Figure 4. Non-metric multidimensional scaling (NMDS) ordination plot (based on a Bray-Curtis similarity matrix) of (A) family abundance on ant mound distance density (MD) (Stress = 0.185) and (B) biomass on ant mounds (Stress =0.189), with fitted vectors and smoothed surfaces. The arrow points to the direction of most rapid change in the variable, while the length of the arrow is proportional to the correlation between ordination and variable. The significance of vector fits was determined using permutation tests (n = 999) at the p = 0.05 level. The blue contour lines correspond to density ant mounds’, fitted with GAM, P<0.001 for both abundance and biomass.

3.3 Influence of ant mounds on vegetation A linear or non-linear regression model was fitted, to predict the influence of distance from the ant mounds on for different types of vegetations (marshland, grassland/low shrubs, and forest). Seventeen of the 42 ant mounds were in marshland vegetation type. Grasses spp. (P=0.022, R2= 0.051), litter (P=0.002, R² =0.082) and Vaccinium uliginosum (P<0.001, R² =0.18) showed an increase in abundance closer to the ant mound, whereas moss Sphagnum spp. (P=0.016, R² =0.056), other mosses (P>0.001, R² =0.065), rushes spp. (P=0.031, R² =0.043), and lichens spp. (P=0.010, R² =0.082), decreased in abundance closer to the ant mound (Figure 5; Appendix II).

Figure 5. Effect of distance from ant mound on vegetation in marshland (n=17). Only significant relationship are shown (P<0.05). For function of each significant relationship, see appendix II.

Twenty-one ant mounds were found in the grassland/low shrubs vegetation type. Grasses spp. (P=0.041, R² = 0.032), litter (P=0.005, R² =0.064), humus layer (P=0.038, R² =0.132), bare ground (P=0.011, R² =0.047) and Vaccinium uliginosum (P<0.001, R² =0.081) showed decreasing abundance with distance from ant mound, whereas moss Sphagnum spp. (P=0.015, R² =0.056), and rushes spp. (P=0.028, R² =0.049), lichens spp. (P=0.028, R² =0.049), decreased in abundance closer to the ant mound (Figure 6; Appendix II).

Figure 6. Effect of distance from ant mound on vegetation in grasslands. Only significant relationship are shown (P<0.05). For function of each significant relationship, see appendix II.

Only 5 ant mounds were found in the forest, and there were no significant relationships between ant mound distance and the vegetation community in the forest.

From the total 210 plots along the gradient Salix spp. occurred in 14.1%, Betula nana in 55.3%, Betula pubescens in 2.3%, Vaccinium uliginosum in 62.7%, Vaccinium vitis-idaea in 45.5%, Empetrum nigrum in 76.7%, Rubus chamaemorus in 3.7%, Equisetum spp. in 20.4%, Andromeda polifolia in 21.8%, forbs in 16.27%, sedges in 44.6%, grasses in 47.9%, rushes in 18.1%, mosses in 22.7%, mosses Sphagnum spp. in 62.32%, lichen spp. in 30.2%, and fungi in 0.46%.

3.3.1 Vegetation on top of ant mounds On average the ant mounds had a vegetation cover of 28.9%. There were no mounds inventoried that had no vegetation present. Of all 16 species/groups making up the vegetation cover on the ant mounds in this study, the 4 most abundant species (i.e. Betula nana, Vaccinium ulginosum, Vaccinium vitis-idaea, and Empetrum nigrum) accounted for 74.4% of all vegetation cover (Table 4). Grasses was the most frequently found group (84.6%).

Table 4. List of plant species observed on the ant mound (n=42), with relative abundance and relative frequency of occurrence. Species Relative frequency of Relative abundance occurrence (%) (%)

Vegetation cover 100 28.9 Betula nana 71.8 6.1 Betula pubescens 2.6 5 Vaccinium_uliginosum 82.1 4.9 Vaccinium vitis idaea 56.4 6.6 Empetrum nigrum 82.1 9.8 Rubus chamaemorus 2.6 3 Andromeda polifolia 10.3 1.3 Salix spp. 10.3 1.7 Equisetum spp. 51.3 1.7 Sedges spp. 76.9 1.8 Grasses spp. 84.6 3.4 Rushes spp. 28.2 1.4 Forbs spp. 10.3 5.6 Moss spp. 5.1 2.1 Moss Sphagnum spp. 7.7 2.1 Lichen spp. 2.6 1

The nest material composition did differ between ant species (P=0.004, R=0.163) and vegetation type (P=0.001, R=0.194), but was not influenced by mound size (P=0.208, R=0.025). In contrast, composition of the vegetation cover on top of mounds differed with mound size (P=0.002, R=0155), but not among ant species (P=0.788, R=-0.038) or vegetation types (P=0.196, R=0.045).

Vole droppings, Andromeda polifolia needles, leaves, branches, Empetrum nigrum, and graminoids made up 70% of the difference between vegetation types and ant species (Appendix III). Vole droppings, Andromeda polifolia needles and branches were on average the most common material found, and mosses, lichen and forbs were the least common materials (Appendix IV). Plant species/groups on mounds that at least contributed for 70% of the difference between mound size were Vaccinium vitis-idaea, Vaccinium uliginosum, grass spp., Empetrum nigrum, and Betula nana (Appendix III).

Variation in vegetation composition on mounds in relation to mound material composition was effectively summarized by a NMDS ordination with fitted vectors (stress = 0.169; Figure 7). All the material in the nest were statistically significant (P<0.05) (Appendix V). Vaccinium vitis-idaea, grass spp., Betula nana, and Empetrium nigrum cover on the nest were negatively correlated with Andromeda polifolia needles. Amounts of vole droppings and branches were positively related with cover of Vaccinium vitis-idaea and Empetrum nigrum. Amounts of leaves and Empetrum nigrum material also increased with cover of Empetrum nigrum. Graminoid material were positive correlated with material from Betula nana and grass spp. Ant mounds were categorized according to the ant species found in the ground survey plots. Note that Andromeda polifolia material was week associated with ant mounds from Formica exsectra (Figure 7). No nest material is correlated with Formica lugubris and Formica uralensis.

Figure 7. Non-metric multidimensional scaling (NMDS) ordination plot (based on a Bray-Curtis similarity matrix), with the environmental variables fitted as vectors (stress = 0.169). Vector fitting of environmental variables showing the relationship between nest material and vegetation composition on top of the nest. The significance of vector fits was determined using permutation tests (n = 999) at the p = 0.05 level.

3.4 GLM models ant mound abundance and volume Ant mound abundance was best explained by a combination of NDVI (p=0.031), Wind Exposition Index (p=0.010), and Annual insolation (p=0.001), where wind showed a negative influence and the other variables showed a positive influence (Table 5).

Table 5. Parameter values best model predicting ant mound abundance. β-values, standard errors, t values and p values for each predictors parameter that is significant (p<0.05) on ant mound abundance. Coefficient Std.error Z Sig. CI. Lower CI.Higher Relative (β) value importance Intercept 2.311 6.620 0.349 0.727 -10.614 15.340 -

NDVI 6.153 2.861 2.151 0.031 0.816 11.966 10.02% - Wind -13.514 5.283 2.558 0.010 -24.227 -3.447 46.34% Annual insolation 0.022 0.006 3.298 >0.001 0.010 0.036 43.64%

Ant mound volume was best explained by a combination of NDVI (p=0.002), Vegetation Height (p=0.001) and Annual insolation (p=0.001), which all showed positive influence on the ant mound volume (Table 6).

Table 6. Parameter values best model predicting of volume ant mounds (AIC = 1414.475). β-values, standard errors, t values and p values for each predictors parameter that is significant (p<0.05) on ant mounds volume. Coefficient Std.error Z Sig CI. Lower CI.Higher Relative (β) value importance Intercept -0.290 0.085 -3.4 >0.001 -0.458 -0.123 -

NDVI 0.249 0.079 3.149 0.002 0.094 0.404 21.93%

Vegetation

Height 0.168 0.034 4.885 >0.001 0.101 0.236 60.72% Annual insolation 0.001 0.001 3.567 >0.001 0.001 0.001 17.34%

For the environmental values of the ground plots in the HA and LA site, it can be noticed that plots with no ant mound was probably due to low values of annual insolation (139.10 ± 28.61) and wetness (18.04 ± 32.63) and too high values of wind (1.01 ± 0.01) and altitude (689.21 ± 31.68 m) (table 7). The Formica exsecta, compared to the other Formica species, was mostly dominant in plots close to the forest edge (8.1±5.28) with high annual insolation (198.08 ± 1.46) and low NDVI (0.75 ± 0.44), while Formica lugubris was mostly dominant in wetter plots (178.68 ± 9.78) with high vegetation height (0.11 ± 0.07), and Formica uralensis was found in drier plots (78.73 ± 105.65) with low vegetation height (-0.03 ± 0.02).

Table 7. An overview of the average environmental variable value from plots were the Formica specie was dominant Index Formica exsecta Formica lugubris Formica uralensis None NDVI 0.75±0.44 0.79±0.07 0.79±0.04 0.78±0.05 Attitude (m) 590.14±41.80 585.04±45.33 605.78±107.13 689.21±31.68 Vegetation -0.01±0.04 0.11±0.07 -0.03±0.02 -0.05±0.02 height index Annual 198.08±1.46 161.23±29.21 165.93±41.49 139.10±28.61 Insolation Wind exposition 0.96±0.01 0.98±0.03 0.97±0.04 1.01±0.01 index Wetness index 158.67±15.80 178.68±9.78 78.73±105.65 18.04±32.63 Distance from 8.1±5.28 11±4.52 13.3±4.88 30.71±1.11 trees (m)

4. Discussion This study found that the presence of wood ants did have substantial effects on vegetation composition within 4 meters from the ant mound in the Arctic tundra. Ant mounds had a positive effect on different kinds of vascular plant species, and a negative effect on rushes, mosses and lichens. Spiders, beetles, grasshoppers, true bugs, harvestmen, and molluscs were all positively affected by ant mound presence. Further, ant mounds were present and larger in areas with higher insolation and GPP values, which indicates that an increase in temperature, caused by climate change, will increase the total mound abundance and volume in the Arctic tundra, with subsequent effects on vegetation and arthropod communities.

4.1 Influence of wood ants on the vegetation My findings are in agreement with the first hypothesis, which stated that vegetation structure changes over distance from the ant mound. However, this influence of ant mounds was not different among vegetation types (Figure 5; Figure 6), which is in contrast to the first hypothesis. The higher abundance of grasses and vascular plant species close to the ant mounds is probably due to wood ants protecting the plants from herbivorous insects and higher nutrients in the near surrounding (Skinner & Whittaker, 1981; Risch & Carroll, 1982; Fowler & MacGarvin, 1985; Perfecto & Sediles, 1992; Mahdi & Whittaker, 1993; Schmitz et al., 2000; Speight et al., 2008; Lenoir et al., 2001). The study of Atlegrim (2015) found similar result on Vaccinium myrtillus in the boreal forest. They stated that wood ants increase the reproductive success of Vaccinium myrtillus through predation of herbivorous insect. Higher nutrients also likely explain why there was lower abundance of mosses and lichens close to the ant mound, as mosses and lichens often dominate surface layers that are poor in nutrients (Petal, 1978; Mandel & Sorenson, 1982). The dispersal of seed by ants can also play a role in shaping the vegetation structure around the mounds (Beattie, 1985). Previous studies have found an accumulation of seeds closer to ant mounds (Dauber, 2006; Schütz et al., 2008). However, predation by ants on seeds of herbaceous plants can also have a negative effect on certain plant taxa around the mound (Wardle et al., 2011), and this could explain the low occurrence of forbs in the plots. Noticeably, it has been shown that some ants species discard myrmecochorous seeds outside the border of the territory after they have retrieved the elaisome (Gorb et al., 2000). Thus, for myrmecochorous plants, it can be difficult to find effects of ants in the near surrounding of the ant mound. Effects of ants can also depend on the environment. For example, Beattie & Culver (1973) found a clear difference in how wood ants influence vegetation in different vegetation types, as there was a negative effect on plant species richness and diversity in juniper dominated forest, whereas the effect was positive in areas dominated by grasses.

The increase of humus layer and litter cover closer to the ant mound can be explained by plant biomass and abundance being higher close to the ant mound, due to the increased presence of vascular plant species, which decreases the amount of bare ground and increases the litter amount and therefore the humus thickness (Berg & McClaugherty, 2003).

4.2 Wood ants’ interaction with arthropod community The results from the arthropod analysis supported my second hypothesis, i.e. that wood ants in the Arctic tundra influence the arthropod community, but the response I found on the arthropod community was contradictory to what was expected. It was expected that wood ants would have negative effects, by excluding competing predators (spider, harvestmen and ground beetle) and preying on herbivory insects (Skinner, 1980; Brüning, 1991; Halaj et al., 1997; Hawes et al., 2013; Reznikova & Dorosheva, 2004), but I found an increased abundance and biomass closer to the ant mound for most taxa (Figure 5; Figure 6). However, although studies showing positive effects of ants are few, similar studies have found no negative effect of wood ants on arthropod communities (Neuvonen et al., 2012; Brüning, 1991; Lenoir et al., 2003; Laakso & Setälä, 2000). Further, it is believed that wood ants can have a strong effect on the arthropod community structure in the canopy (Liere &

Perffect, 2008; Billick et al., 2007; Styrsky & Eubanks, 2007; Warrington & whittaker, 1985), whereas effects on ground dwelling arthropods are weaker (Lenoir et al., 2003; Laakso & Setälä, 2000). Thus, the positive effect of ants that I found is probably explained by shared environmental preferences rather than by direct beneficial effect of wood ants. Nevertheless, because wood ants are opportunistic predators it still can be assumed that they have a large influence on the arthropod community in the Arctic tundra (Horstmann, 1972), but on a smaller scale than was investigated in this study.

Sanders & Platner (2006) observed that higher density of ants negative affected the web- building spiders, which is similar to my results. The difference between spider groups in effect of ant mound can be explained by ground spiders easily moving around to avoid detection by ants, whereas web-building spiders are mostly stuck in one location and are therefore more easily detected (Sanders & Platner, 2006). Moreover, Riechert & Tracy (1975) found that web-builder spiders place their webs in location with the highest survival potential, rather than location with high prey availability (Riechert & Tracy 1975). What suggest that web-building spiders are avoiding the close surrounding of the ant mound, even if the prey availability is high.

4.3 Influence of climate change on the wood ants The third hypothesis, that GPP (expressed as NDVI) influence ant mound abundance and volume, can be assumed valid (Table 5; Table 6). My regression analyses showed that ant mounds are more numerous and larger in areas with higher values of GPP in the Arctic tundra. Also, other factors, such as annual insolation and wetness, had a similar correlation with the ant mounds. Both these factors will be influenced by climate change (Chapin, 1983; Rustad et al., 2001; Hinzman et al., 2005; Aerts, 2006). My results are similar to the study by Stockan et al. (2010), who showed an importance of light and vegetation for mound location of Formica exsecta. As such, my results support the findings of earlier studies, which in that annual insolation is the most important factor for determining the ant mound presence on the local scale (Stockan et al., 2010; Maggini et al., 2002).

Mound volume can act as a proxy for colony size (Tschinkel, 1995; Chen & Robinson, 2013). Hence, the observed positive influence of insolation and wetness on mound volume indicates that future climate change will result in increased colony size. In turn, this will dramatically affect the territory size and foraging pressure of individual ant mounds and their impact on their surrounding environment (Tschinkel, 1995; Thomas & Elgar, 2003; Gordon, 2010). Climate change will also alter the ant colony behaviour, as ant foraging activity is strongly linked to ambient temperature and prey selection (Traniello, 1984; Vogt et al., 2003; Stuble, 2013), and will therefore drastically increase the impact of wood ants on surrounding vegetation and arthropod communities.

Wood ant nest are hotspot of co2 production in the boreal forest (Ohashi et al., 2005; Domisch et al., 2006). When the ants are active the carbon efflux is 2.6-7.8 times higher from the ant mound compared to the surrounding soil (Domisch et al., 2006). Hence, an increase in the amount of ant mounds due climate change can also increase the co2 released from the soil in the tundra. To be able to make accurate predictions how an increase in temperature and productivity will affect the co2 output from ant mounds, ant colonies and their influence on the ecosystem needs to be studied more in depth in the Arctic tundra.

In the study site the Formica exsecta was open areas close to the forest, while the Formica lugubris was common in wetter areas with high vegetation and Formica uralensis in higher altitude areas with short vegetation (Table 7). Hence, because climate change is expected to change vegetation composition, it will likely also alter the wood ant composition in the Arctic tundra. Accordingly, an increase in soil temperature was found to radically affect the abundance and distribution of Formica exsecta in the Subarctic (Alfimov et al., (2010). This will have drastic effect on some of the Formica species, such as Formica uralensis, which is

18 more specialized in areas with shorter vegetation and higher in attitude. Increases in temperature can also impact the distribution range of invasive ant species, with the potential for cold-intolerant species to establish themselves and compete with the native species in the Arctic tundra (Ellison, 2012; Morrison et al., 2004).

4.4 Vegetation on the mound Nest material foraged by the wood ants is location specific and determines the vegetation structure on the ant mound. Grasses were the most common taxa found on top of the ant mounds (Table 2), which supports previous studies showing that active ant mounds were dominated by rhizomatous graminoids, and that abandoned mounds were hotspots for shrubs (Lesica & Kannowski, 1998). Andromeda polifolia material had the strongest negative effect on the vegetation composition on the mounds and vole dropping the strongest positive effect. Which is probably due to the low nitrogen and phosphate concentration of Andromeda polifolia (Jacquemart, 1998), and the high nutrient concentration in vole droppings (Figure 7). The effect of mound size on vegetation composition is likely due to larger ant mound tending to be older, giving vegetation more time to establish.

4.5 Methodology Despite the substantial amount of measurements taken in this study, there is always room for improvement. The low variation explained (R²<0.01) by distance to ant mounts on the vegetation cover data indicates that there are other environmental factors in play, which were not accounted for in the statistical analyses. For example, wood ants occur more frequently in locations with more sunlight and are presumably therefore avoiding habitats dominated by Betula pubescens because it blocks the sunlight from reaching the ground (Hölldobler & Wilson, 1990). It is, however, also possible that the effect of wood ants on the vegetation went beyond the 4-meter transects I used, which therefore rendered distance to ant mound a poor predictor variable. Moreover, the occurrence of some vegetation groups, i.e. Rubus chamaemorus, Betula pubescens, and forbs, as well as fungi, was too low (<30 plots) for me to reach good conclusions based on the data material. Further, all vegetation types were unequally represented in the different habitats (i.e. grassland/shrubs, forest and marshland), which disabled me from analyzing if the effect of ant mounds was habitat specific. Hence, more research is necessary, using longer transects, more samples in every vegetation type, and more samples through the growing season, to better understand how wood ants influence the vegetation in the Arctic tundra.

Larger mounds that occurred in areas with denser and taller vegetation had a higher detection probability in the RGB (G9x) map. Smaller ant mounds were 12 times less likely to be detected by drones than were larger ant mounds. This could have resulted in an underrepresentation of (small) ant mounds in certain vegetation types. Further, it was not expected that the higher altitude site would have such a low number of ant mounds, which resulted in this site being excluded from some of the analysis.

4.6 Conclusion My study demonstrates that ant mounds influence the vegetation and arthropod composition in the Arctic tundra, and that climate change can be an important driver of this influence. Most likely, a climate-change driven increase in temperature will first change the plant community and the wood ant abundance (Figure 8), as the ants to some extent are dependent on the presence of certain vegetation types (Stockan et al., 2010). Then, the ants will, in turn, affect the arthropod and plant communities. In this scenario, and based on my results, plants like grasses, Vaccinum ulginium will benefit, while other groups, such as mosses and lichens, will be negative affected. The increase in the abundance of ants will also decrease competing predators and herbivory insects (Skinner, 1980; Brüning, 1991; Halaj et al., 1997; Hawes et al., 2013; Reznikova & Dorosheva, 2004). More mounds will also increase

19 the amount of nutrient rich hotspots (Petal, 1978; Mandel & Sorenson, 1982; Lenoir et al., 2001; Ohashi et al., 2007; Kilpeläinen et al., 2007). An increase in ant mounds will over time also contribute to an increase in landscape heterogeneity in the Arctic tundra (Carlson & Whitfold, 1991).

Figure 8. A summary of the result found in this study and from literature about the relation that ants have with the ecosystem in the Arctic tundra. The thickness of the arrow indicates how strong the influence is. 1: Environmental and climate variables have a strong direct influence on ant mound abundance, volume, and ant activity, but also influence ants indirectly by having impacts on the vegetation structure and other arthropods. 2: Ant mounds have a strong influence on the vegetation by leaching nutrients and by ants harvesting seeds and plant material. The nest material composition is in turn influenced by the available vegetation type. 3: Arthropods show higher abundance and biomass closer to the ant mounds, likely because of shared environmental preferences. 4: Ants influence ant mounds by foraging vegetation and collecting nest material

Acknowledgement First, I would like to thank my supervisor Micael Jonsson, for providing me with his feedback and support. Your guidance has been invaluable, especially in the last weeks. Also, I am grateful for the for the assistance of Matthias Siewert in the field and helping me with GIS. Last but certainly not least, I want to thank all the interns that helped me during my field work is Abisko.

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Appendix I: Study sites

Figure 9. A landscape overview of the lower attitude study site (LA). The site is dominated with low shrubs and grasses, with here and there birch trees.

Figure 10. A landscape overview of the higher attitude study site (HA). The site is dominated with grasses and rocks/bare ground.

Appendix II: Equations for distance to ant mound to vegetation

Table 8: The table shows the mathematical function and its correspondent equation having the best fit per each plant species/group marshland vegetation type. Only significant mathematical functions are shown (P<0.05). P- value and r² are shown for every plant species/group.

Area Function Equation P-value r² Grasses spp. Linear 0.191765+-0.025882*x 0.0225 0. 051 Rushes spp. Linear -0.0017647 +-0.0100000 *x 0.0316 0. 043 Vaccinium uliginosum Log 0.424758-0.184358*log(x) <0.0001 0.18 moss sphagnum spp. Log 0.111406+0.180388*log(x) 0. 0161 0. 056 Moss spp. exponential 0.00002587*exp(1.4559*x)+ 0.016173 <0.0001 0. 065 Litter quadratic -0.0218487*x^2+0.1499160*x-0.12 0.0028 0.082 Lichen spp. quadratic -0.791765*x^2+0.029412*x-0.230588 0.0107 0.082

Table 9. The table shows the mathematical function and its correspondent equation having the best fit per each plant species/group in grassland/low shrubs vegetation type. Only significant mathematical functions are shown (P<0.05). P-value and r² are shown for every plant species/group. Area Function Equation P-value r² Grasses spp. Log 0.161580-0.052377*log(x) 0.0421 0. 032 Rushes spp. quadratic -0.0142857*x^2+0.0819048*x-0.0542857 0.0286 0. 049 Vaccinium uliginosum exponential -0.37255*exp(-0.768216*x)+ 0.099780 0.0048 0.081 moss sphagnum spp. linear 0.100476+0.046190*x 0. 015 0. 046 Bare ground quadratic -0.052381 *x^2+0.323619 *x-0.169333 0.0112 0. 047 Litter linear 0.672381-0.060000 *x 0.0052 0.064 lichen spp. quadratic -0.024150 *x^2+0.162517 *x-0.134286 0.0285 0. 049 Humus layer quadratic 8.9966*x^2+50.6701*x-156.7619 0.0138 0.132

Appendix III: Output from SIMPER Error! Bookmark not defined.

Table 10. SIMilarity PERcentage (SIMPER) analysis of the plant species/groups that at least contributed for 70% of the difference between mound size Group: Large-Small Species Abundance large % Abundance Small % % Contribution Empetrum nigrum 0.089893 0.051231 0.229 Betula nana 0.052267 0.0135 0.4 Vaccinium vitis-idaea 0.067367 0.006846 0.5705 Vaccinium uliginosum 0.045493 0.024115 0.689 Grass spp. 0.043893 0.008354 0.806

Group: Large-Medium Species Abundance Large % Abundance Medium % % Contribution Empetrum nigrum 0.089893 0.103991 0.239 Betula nana 0.052267 0.066909 0.436 Vaccinium vitis-idaea 0.067367 0.032682 0.6077 Vaccinium uliginosum 0.045493 0.052455 0.7224

Group: Small-Medium Species Abundance Small Abundance Medium % Contribution Empetrum nigrum 0.051231 0.103991 0.2798 Betula nana 0.0135 0.066909 0.4667 Vaccinium vitis.idaea 0.024115 0.052455 0.6231 Vaccinium uliginosum 0.006846 0.032682 0.7302

Table 11. SIMilarity PERcentage (SIMPER) analysis of the mound material that at least contributed for 70% of the difference between vegetation types Groups: Grassland-Swamp Material Abundance Grassland Abundance Swamp % Contribution Rest material 0.547113 0.537754 0.2773 Andromeda polifolia 0.119716 0.150439 0.5038 Vole droppings 0.060061 0.06859 0.6251 Branches 0.099844 0.076588 0.7436

Groups: Grassland-Forest Material Abundance Grassland Abundance Forest % Contribution Rest material 0.547113 0.533287 0.2618 Andromeda polifolia 0.119716 0.000355 0.4385 Vole droppings 0.060061 0.113236 0.5859 leaves 0.039213 0.112648 0.7098

Groups: Swamp-Forest Material Abundance Forest Abundance Swamp % Contribution Rest material 0.537754 0.533287 0.2256 Andromeda polifolia 0.150439 0.000355 0.4374 leaves 0.033428 0.112648 0.5664 Vole droppings 0.06859 0.113236 0.6917 Branches 0.076588 0.112318 0.8045

Table 12. SIMilarity PERcentage (SIMPER) analysis of the mound material that at least contributed for 70% of the difference between ant species. Groups: Formica exsecta - Formica uralensis Material Abundance exsecta Abundance uralensis % Contribution Rest material 0.566943 0.512172 0.2688 Andromeda polifolia 0.150753 0.079976 0.4662 Brachens 0.06543 0.12155 0.5953 Vole droppings 0.045909 0.091334 0.7177

Groups: Formica exsecta - Formica lugubris Material Abundance exsecta Abundance lugubris % Contribution Andromeda polifolia 0.150753 0.114197 0.2558 Rest material 0.566943 0.522812 0.5107 Vole droppings 0.045909 0.090095 0.6312 Empetrum nigrum 0.034483 0.042326 0.7367

Groups: Formica lugubris - Formica uralensis Material Abundance lugubris Abundance uralensis % Contribution Rest material 0.512172 0.522812 0.285 Andromeda polifolia 0.079976 0.114197 0.4855 Vole droppings 0.091334 0.090095 0.6262 Branches 0.12155 0.096243 0.7459

Appendix IV: Average values nest material between groups

Table 13. List of material found in the ant mounds (n=42), with relative proportion per ant species and vegetation type. Formica Formica Formica Forest Grassland/ marshland Exsecta Lugubris Uralensis shrubs Rest 56.74% 53.12% 53.76% 53.33% 55.49% 56.02% Vole droppings 5.26% 9.53% 7.18% 11.32% 6.34% 6.25% Polifolia needles 11.58% 11.03% 10.00% 0.04% 10.38% 13.56% Betula nana leaves 0.72% 0.80% 1.19% 0.26% 1.06% 0.95% Rocks 3.97% 1.62% 3.70% 0.28% 3.05% 4.28% Leaves 2.89% 5.48% 5.24% 11.26% 3.79% 3.36% Moss 1.05% 1.19% 1.66% 1.54% 1.29% 1.13% Graminoids 3.14% 2.88% 2.64% 2.13% 3.08% 2.74% Branches 9.93% 9.43% 10.03% 11.23% 10.86% 8.03% Empetrium nigrum 3.63% 3.85% 3.34% 6.93% 3.66% 2.59% Forbs 0.79% 1.05% 1.22% 1.41% 0.91% 0.92% Lichen 0.29% 0.01% 0.04% 0.26% 0.09% 0.18%

Appendix V: Fixed vectors for nest material

Table 14. Environmental vectors significantly correlated with the vegetation on top of the nest based on the envfit vector-fitting.

Variable NMDS1 NMDS2 R2 P Vole Droppings 0.99746 0.07117 0.3388 0.001 Andromeda polifolia material -9.79E-01 -0.20562 0.7823 0.001 Leaves material 0.57568 -0.81768 0.3204 0.001 Graminoids material -0.21 0.9777 0.2876 0.006 Branches 0.98239 -0.18687 0.2973 0.003 Empetrum nigrum material 0.70982 -0.70438 0.1863 0.033

Appendix VI: Invertebrate abundance and biomass for both sites

Table 15. Abundance and biomass of taxonomic ordes of both study sites.

Abundance Biomass LA HA LA HA Aranea 362 194 1292.04 1101 Coleoptera 56 111 253.44 330.38 Hemiptera 14 24 1.66 20.08 Mollusca 52 24 198.52 96.3 Opiliones 68 167 439.04 1018.08 Orthoptera 28 26 1856.78 106.9 Diptera 440 570 462.84 379.2 Hymenoptera* 802 130 967.16 94.58 Lepidoptera 4 28 22.14 38.12 Lepidoptera larvae 8 32 196.14 62.9 Coleoptera Larvae 22 29 35.74 323.44 Total 1856 1335 5725.5 3570.98 * without Formicidae

Table 16. Abundance and biomass of taxonomic families of both study sites.

Abundance Biomass LA HA LA HA Apionidae 7 7 42.22 34.4 Carabidae 36 46 156.08 230.3 Chrysomelidae 5 0 15.34 0 Curculionidae 7 12 41.46 2.34 Elateridae 8 4 8.8 3.04 Formicidae 732 28 865.6 22.68 Gnaphosidae 24 24 239.4 228.16 Linyphiidae 30 52 11.44 20.2 Lycosidae 298 112 1037.8 816.96 Staphylinidae 8 44 17.54 93.3 Thomisidae 8 6 8.78 27.48 Zoridae 6 0 3.2 0