Mapping of arthropod alpha and beta diversity in heterogeneous arctic-alpine ecosystems

Journal Pre-proof Mapping of arthropod alpha and beta diversity in heterogeneous arctic-alpine ecosystems

Nils Hein, Jörg Löffler, Hannes Feilhauer PII:

S1574-9541(19)30318-8

DOI:

https://doi.org/10.1016/j.ecoinf.2019.101007

Reference:

ECOINF 101007

To appear in:

Ecological Informatics

Received date:

26 March 2019

Revised date:

5 September 2019

Accepted date:

6 September 2019

Please cite this article as: N. Hein, J. Löffler and H. Feilhauer, Mapping of arthropod alpha and beta diversity in heterogeneous arctic-alpine ecosystems, Ecological Informatics(2019), https://doi.org/10.1016/j.ecoinf.2019.101007

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© 2019 Published by Elsevier.

Journal Pre-proof Mapping of arthropod alpha and beta diversity in heterogeneous arctic-alpine ecosystems Nils Heina,d | Jörg Löfflera | Hannes Feilhauerb, a

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University of Bonn, Department of Geography, Meckenheimer Allee 166, D-53115 Bonn,

Germany b

FAU Erlangen-Nürnberg, Institute of Geography, Wetterkreuz 15, D-91058 Erlangen, Germany

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FU Berlin, Institute of Geographical Sciences, Malteserstr. 74-100, D-12249 Berlin, Germany

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Ilia State University, School of Natural Sciences and Engineering, 3/5 Choloqashvili Ave, Tbilisi

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0162, Republic of Georgia Corresponding author: Nils Hein

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University of Bonn, Department of Geography, Meckenheimer Allee 166, D-53115 Bonn, Germany

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Email: [email protected]

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Journal Pre-proof Abstract Assessing biodiversity in arctic-alpine ecosystems is a costly task. We test in the current study whether we can map the spatial patterns of spider alpha and beta diversity using remotely-sensed surface reflectance and topography in a heterogeneous alpine environment in Central Norway. This proof-of-concept study may provide a tool for an assessment of insect communities in remote study areas. Data on arthropod species distribution and richness were collected through pitfall trapping and subjected to a detrended correspondence analysis (DCA) to extract the main species

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composition gradients. The DCA axis scores as indicators of species composition as well as trap species richness were regressed against a combined data set of surface reflectance as measured by

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the Sentinel-2 satellite and topographical parameters extracted from a digital elevation model. The

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models were subsequently applied to the spatial data set to achieve a pixel-wise prediction of both

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species richness and position in the DCA space. The spatial variation in the modelled DCA scores was used to draw conclusions regarding spider beta-diversity. The species composition was

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described with two DCA axes that were characterised by post hoc-defined indicator species, which

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showed a typical annidation in the arctic-alpine environment under study. The fits of the regression models for the DCA axes and species richness ranged from R² = 0.25up to R² = 0.62. The resulting

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maps show strong gradients in alpha and beta diversity across the study area. Our results indicate that the diversity patterns of spiders can at least partially be explained by means of remotely sensed data. Our approach would likely benefit from the additional use of high resolution aerial photography and LiDAR data and may help to improve conservation strategies in arctic-alpine ecosystems. KEYWORDS digital elevation model (DEM), predictive mapping, remote sensing, Sentinel-2, araneae, tundra

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1 | INTRODUCTION Arthropods are the most diverse meta-taxon on Earth, and arctic and alpine environments, especially, are often dominated by arthropod species (Roslin, et al., 2013; Schmidt, et al., 2017). It is well recognized that arthropod diversity and assemblage composition in the Northern hemisphere will be strongly impacted by environmental changes over the next few decades (Koltz, et al., 2018). In fact, alterations to arctic-alpine ecosystems are already visible at all ranges from local to regional scales (e.g. Parmesan & Yohe, 2003, Kausrud et al., 2008, Hobbie, et al., 2017). Due to their high

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climate sensitivity (Høye, et al., 2009; Loboda, et al., 2018) and relatively short life-spans compared to many other organisms, arthropods should be considered suitable model organisms for detecting

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changes in arctic and alpine ecosystems. Arthropods are known as biological indicators of

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environmental heterogeneity (McIntyre et al., 2000; Dennis et al., 2002; Lenoir & Lennartsson

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2010; Lafage et al. 2019), and alpine habitats are often compared to islands of arthropod biodiversity because they provide habitat and refuges for species that are out-competed at lower

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elevations in the surrounding areas (e.g. Graham, et al., 2014; Slatyer & Schoville, 2016). Spiders

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are generalist predators, and in alpine and arctic environments, they play a crucial role in the

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associated food webs (Wirta, et al., 2015; Schmidt, et al., 2017), especially in connecting the above and below-ground food webs (Scheu, 2001). In mountainous areas, rapid changes along biogeographical gradients are visible that result in pronounced differences in factors such as climate, soil, or vegetation. This results in large habitat variability at relatively short distances (Körner, 2007; Nagy & Grabherr, 2009; Wundram, et al., 2010). Mountainous areas are characterised by complex topography and huge geodiversity, which commonly results in high biodiversity due to high niche availability (Steinbauer et al., 2016; Badgley et al., 2017; Mosbrugger et al., 2018). This makes mountainous areas interesting for exploring the underlying processes that produce the spatial patterns of species distribution and richness. The elevational gradients provide an excellent means for studying the effects of environmental conditions on biodiversity (Rahbek, 2005; Sanders & Rahbek, 2012), and assessing

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Journal Pre-proof biodiversity at local to regional scales requires tools that enable the detection of change, even in relatively remote areas. A cost-effective and accurate assessment of the biodiversity in arctic-alpine ecosystems using data derived from remote sensing may provide such a tool for all scientists working in these environments. Several studies have shown the potential of satellite-derived vegetation indices for analysing species richness patterns in different animal groups (e.g. Lassau, et al., 2005; Pettorelli et al., 2011; Lafage, et al., 2014). However, such approaches have not yet been fully tested for assessing the spatial patterns of spider diversity and assemblage composition. To

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fully exploit the potential of arthropods in ecological studies, a reliable and efficient tool to identify

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distribution patterns is needed. We thus present a proof-of-concept study testing the ability to

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analyse arthropod diversity in an arctic-alpine ecosystem by means of remote sensing and predictive

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mapping. In particular, we test whether we can map the spatial patterns of spider alpha and beta diversity based on surface reflectance and topography in a relatively heterogeneous environment.

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To this end, we conducted a case study in an arctic-alpine environment using spider trap data in

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digital elevation model (DEM).

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combination with Sentinel-2 optical remote sensing data and topographical layers derived from a

2.1 | Study area

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2 | MATERIALS AND METHODS

The study was implemented as part of the long-term alpine ecosystem research (LTAER) project in central Norway (61°53′ N, 9°15′ E; Vågå, Oppland) along an elevational gradient reaching from the tree line up to 1617 m a.s.l. during the snow-free period in 2009. According to Moen (1998), the study area is part of the weak continental section (C1) in Norway. The annual precipitation values are some of the lowest found in Norway, with values of approximately 300–400 mm in the valleys (Löffler & Finch, 2005). The alpine environment begins above the treeline at ~1030 m a.s.l. A transition zone between the low- and middle-alpine belts is located at ~1350 m a.s.l. Summer in the low-alpine belt is characterised by 13–15 snow-free weeks compared to only 11–12 snow-free

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Journal Pre-proof weeks in the middle-alpine belt (Löffler, 2002). The study area is characterised by an average of 150–170 days with temperatures ≥ 5 °C, whereas the yearly average temperature is 6 °C (Moen, 1998). Snow-cover thickness and duration, negatively correlated to season length, are the key controlling factors for the spatial distribution of vegetation types in this part of the Scandes (Löffler, 2005; Odland & Munkejord, 2008). These factors are mainly a result of the topography and the prevailing winds during the winter and produce a typical organisation of vegetation types, mainly along two gradients: (i) elevational and (ii) topographical.

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Along the elevational gradient, snow-cover thickness and duration increase at higher

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elevations, resulting in an elevational zoning pattern consisting of a low-alpine belt dominated by

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shrub and heather communities and a middle-alpine belt dominated by grassy vegetation (Dahl,

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1986; Fremstad, 1997). At the highest elevations, sparse vegetation occurs only in patches between block fields and debris.

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Along the topographical gradients, winter conditions are characterized by thick snow cover in

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the depressions and more or less snow-free ridges, resulting in zones of vegetation with lichenheath communities at ridge sites, shrub and heather communities at slope sites, and Sphagnum

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mosses or other bryophytes in the depressions (Löffler, 2003). With the exception of some regions

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that are too steep for utilisation, the valleys are mostly used agriculturally. Above the tree line, extensive summer grazing by sheep and/or cattle is the most prevalent land-use form (Rössler, et al., 2008).

2.2 | Field data The survey of ground dwelling spiders was conducted during the snow-free period in 2009. Pitfall traps were used for estimating the spider diversity and abundance along two gradients covering the typical habitats of the alpine areas in this part of the Scandes: (1) an elevational gradient, with the sampling locations covering sites from the tree line up to the highest peak; and (2) a micro-scale topographical gradient covering the main relief positions, concentrating on sites with the most pronounced contrasts in alpine environments: ridges, depressions, south-facing slopes, and north-

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Journal Pre-proof facing slopes (Löffler, 2002; Löffler & Finch, 2005; Nagy & Grabherr, 2009). The sampling locations corresponded to typical situations representative of the specific elevations (m a.s.l.) and topographical positions (e.g. Hein et al., 2014; Beckers, et al., 2018). The elevations of the sampling sites ranged from ~1029 m a.s.l. up to ~1609 m a.s.l., with the exact elevations estimated to the closest 0.1 m with dGPS. Our approach included a total of 40 sampling locations, which were equipped with three pitfall traps each other (total number of traps = 120). The three single pitfall traps were installed in line, no more than approx. 10 m apart from each. The sampled spider

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material from the three pitfall traps were later pooled to get one specific result of the spider

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diversity at each of the 40 sampling locations used as observations in the analysis (n = 40). A

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saturated salt solution was used as the preservative and Agepon as the detergent. The collected

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material was transferred to 70% ethanol for preservation and further processing. The pitfall traps were installed as soon as the sites became snow free and were emptied on a biweekly basis until

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snow cover prevented further sampling. Thus, 11 sampling periods were implemented in our study

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during the summer of 2009 between 28 April and 29 September.

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2.3 | Ordination and species richness data

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The work flow of the analyses is outlined in figure 1. To describe the compositional patterns of the spider species assemblages across the study area, we subjected the sampled field data to an ordination approach, which arranges the 40 trap sites in a multidimensional hyperspace according to the similarity of their inventory. Trap sites with similar species inventories are located nearby, while trap sites with very dissimilar inventories are positioned at the opposing ends of the ordination space. The axes of the ordination space can be interpreted in terms of a gradually changing species composition. In comparison to cluster analyses that result in discrete spider assemblages, the ordination approach accounts for the fuzziness of nature and preserves the gradually changing compositional patterns along environmental gradients. It further allows for analysing the beta diversity between trap sites as indicated by their mutual distances in the ordination space (Feilhauer & Schmidtlein, 2009; Rocchini et al., 2018).

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Various methods can be used for the ordination of species data. Here, we used a detrended correspondence analysis (DCA; Hill & Gauch, 1980), a very common technique in ecology. In comparison to other available approaches, DCA offers a major advantage: the axes of the DCA space are scaled in standard deviation (SD) units. A distance of 4 SDs indicates a full species turnover, i.e. two trap sites with an inter-distance of 4 SD or more have no species in common. This scaling eases the interpretation of the ordination space and allows for a quantitative assessment of

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beta diversity. We performed the DCA based on the abundance data normalized per trap night with

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a down-weighting of rare species to reduce the level of noise in the data. The scores for the trap

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sites on the axes were used in the subsequent analyses as indicators of spider species composition.

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To ease the interpretation of the DCA space, we further subjected the species data to an isopam cluster analysis (Schmidtlein, et al., 2010). The resulting clusters and related indicator species were

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projected in the DCA space and helped to describe the spider assemblages along the DCA axes.

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Furthermore, we determined the total number of arthropod species for each trap site. These species richness data were used in the analyses to describe the spider alpha diversity in addition to

2.4 | Topography

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the DCA scores and derived beta diversity.

The topographical data for the study area was obtained through the use of the ASTER global digital elevation model (DEM), which is a product of METI and NASA and available free of charge through USGS Earth Explorer. It has a spatial resolution of 1 arc second (approximately 30 m × 30 m). The DEM was used to calculate five topographic indices: slope (following Horn, 1981); the cosine of the surface aspect (following Horn, 1981), indicating the exposition towards North; the sine of the surface aspect, indicating the exposition towards East; the topographic positioning index (TPI), describing the elevation of a pixel in relation to the eight surrounding pixels (Wilson, O'Connell, Brown, Guinan, & Grehan, 2007); and the terrain ruggedness index (TRI), describing the difference between the maximum and minimum elevation of a nine-cell neighbourhood (Wilson

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Journal Pre-proof et al., 2007). For the purposes of this study, the DEM and all topographic indices were resampled to a 10 m × 10 m resolution using bilinear interpolation to meet the spatial resolution of the Earth observation data.

2.5 | Earth observation data To describe the composition of the Earth’s surface in the study area, we included a Sentinel-2 (Martimor, et al., 2007) multispectral image acquired on 18 August, 2015. The image is free of

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snow, clouds, and haze and captures the study area close to the vegetation optimum. Sentinel-2

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provides reflectance data in 13 spectral bands covering the visible, near-infrared (NIR), and short-

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wave infrared (SWIR) ranges. Three spectral bands with a spatial resolution of 60 m × 60 m are designed to quantify the atmospheric water and aerosol contents, which were not used in this study;

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the remaining ten bands have a spatial resolution of 10 m × 10 m (bands in the visible region and

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one NIR band) and 20 m × 20 m (all other NIR and SWIR bands). We resampled all bands to a resolution of 10 m × 10 m using bilinear interpolation.

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The data were processed to the top-of-atmosphere reflectance. We tested a topographic

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normalization of the image data using a cosine correction function, but it did not result in an

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improvement of the data and model quality and was hence dismissed. We used the image data to calculate the normalized difference vegetation index (NDVI; Tucker, 1979), the normalized difference red edge vegetation index (NDVIRE; Schuster, et al., 2012), and the normalized difference water index (NDWI; McFeeters, 1996) using the following equations (1–3): NDVI = (NIR [Band 8] – Red [Band 4]) / (NIR [Band 8] + Red [Band 4])

(1)

NDVIRE = (Red edge [Band 5] – Red [Band 4]) / (Red edge [Band 5] + Red [Band 4])

(2)

NDWI = (Green [Band 3] – NIR [Band 8]) / (Green [Band 3] + NIR [Band 8])

(3)

NDVI and NDVIRE are sensitive to the photosynthetic activity of the vegetation and are therefore a proxy for vegetation biomass, leaf area, and vigour. The difference in the bands used in their calculations affects their sensitivity and allows for the consideration of small differences while tackling the issue of index saturation. The NDWI indicates the moisture and water content of a

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2.6 | Analysis We built three separate regression models, one for each DCA axis as well as for the species richness data. For this purpose, the coordinates of the trap sites were used to extract the corresponding pixel values from the image, vegetation indices, and topographic index layers. The resulting site-by-

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parameter table was used as a predictor dataset for the subsequent analyses. Both DCA axis scores

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and species richness data were regressed against this predictor data set using random forest

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regression (RFR) models (Breiman, 2001). For this study, we included 500 regression trees per model, which resulted in exhaustively trained models with a stable model error. The out-of-bag

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(OOB) error was used to quantify the model fits. In addition, we calculated the RMSD following

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the recommendations by Piñeiro et al. (2008). For an assessment of the variable importance in the models, we relies on the increase of node impurity during a permutation of the tested variables. For

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this purpose 4 out of the 19 variables were selected at each node.

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The resulting three RFR models for the two DCA axes and species richness were subsequently applied to the set of spectral bands, vegetation indices, and topographic parameters for a pixel-wise spatial prediction. To create a map of spider beta-diversity, the mapped DCA axes were further processed using a moving window approach with a kernel size of 9 pixels × 9 pixels (i.e. 90 m × 90 m) following the approach described in Feilhauer & Schmidtlein (2009) and Rocchini et al. (2018). Within the window, the total range of the DCA scores of the nine covered pixels on both mapped DCA axes was determined and assigned to the centre pixel. A large range indicates a high beta diversity on the analysed spatial scale. The scaling of the DCA space with 4 SD corresponding to a full species turnover enables a quantitative interpretation of the resulting pattern. Data preprocessing, ordination, modelling, and other analyses were conducted in the R statistical environment (R Core Team, 2018) using the packages ‘raster’ (Hijmans, 2016), ‘rgdal’ (Bivand, et

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Journal Pre-proof al., 2017), ‘vegan’ (Oksanen, et al., 2017), ‘isopam’ (Schmidtlein et al., 2010), and ‘randomForest’ (Liaw & Wiener, 2002).

3 | RESULTS 3.1 | Ordination and species richness per hunting strategy During the 13,065 trap nights in the snow-free season of 2009, a total of 3979 adult specimens (juveniles were excluded) were sampled in the research area (Figure 2). This corresponds to

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approximately 0.30 adult specimens per trap night. The number of species per trap ranged from 6 to

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31 with a mean of 14.25 species. More than 90% of all specimens sampled belonged to two spider

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families: (i) the Linyphiidae with a total of 1588 specimens (belonging to 64 species) and (ii) the

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Lycosidae with a total of 2144 specimens (belonging to 12 species). For the statistical analysis we used the trap night corrected abundances of the species at the respective sampling locations.

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Figure 3 illustrates the distribution of the four isopam-clusters in the ordination space. Trap sites in Cluster 1 are characterized by the post hoc-identified indicator species Bolyphantes luteolus

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(Blackwall, 1833), Oreonetides vaginatus (Thorell, 1872), Pelecopsis mengei (Simon, 1884),

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Tenuiphantes mengei (Kulczyński, 1887), Pardosa atrata (Thorell, 1873), Hilaira nubigena Hull,

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1911, Hilaira pervicax Hull, 1908, Maso sundevalli (Westring, 1851), and Pardosa riparia (C. L. Koch, 1833). The species in this cluster can be summarized and characterized as species occurring in forests or at the treeline with a slight preference for mesic habitats (Hein et al., 2014; Nentwig, Blick, Gloor, Hänggi, & Kropf, 2018). Cluster 2 is characterized by the occurrence of Alopecosa aculeata (Clerck, 1757), which is a typical species in forests that occurs in the study area, especially at slopes (Hein et al., 2014) characterized by a shrub vegetation formed by Vaccinium myrtillus, Betula nana, and Empetrum nigrum ssp. hermaphroditum. Trap sites affiliated with Cluster 3 typically showed occurrences of Agyneta nigripes (Simon, 1884) and Pardosa trailli (O. P.Cambridge, 1873), two typical alpine species (Nentwig et al., 2018) that show higher abundances on ridge sites in our study area. Finally, the characteristic species in the trap sites belonging to

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Journal Pre-proof Cluster 4 are Erigone tirolensis L. Koch, 1872, Collinsia holmgreni (Thorell, 1871), Erigone atra Blackwall, 1833, and Acantholycosa norvegica (Thorell, 1872). These last four are also considered typical alpine species and occur in dry as well as mesic habitats (Hein et al., 2014; Nentwig et al., 2018). The first two axes of the ordination space cumulatively explained 56% of the variance in the original data. The third axis would have explained only another 4% and was therefore dismissed from further analysis. The first axis had a length of 4.6 SD, while the second axis was 3.4 SD long.

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3.2 | Modelling results

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The fits of the three random forest regression models are shown in Figure 4. Based on OOB error estimation, the models explained 25.15% to 62.43% of the original variation in the data. The

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strongest model fit was achieved for the first DCA axis; the model for the second axis had the

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weakest fit.

The variable importance of the individual spectral bands, vegetation indices, and topographic

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parameters in the different models is shown in Figure 5. The DCA1 model mostly builds upon the

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vegetation indices and, to a lesser degree, on surface reflectance in the SWIR and visible blue and

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red ranges. The topographic parameters had minimal influence in this model. Similarly, the DCA2 model is based on surface reflectance in the visible and NIR ranges as well as on the vegetation indices. The model for species numbers is largely based on elevation and SWIR reflectance, with minor influences from surface reflectance in the visible region and the NDVI.

3.3 | Mapped patterns The predicted spatial patterns of the spider assemblages and species richness are shown in Figure 6. Low-lying sites have the highest species numbers and are occupied by spider assemblages with positive scores on DCA1 and negative scores on DCA2. Areas between 1000 and 1500 m a.s.l. with pronounced differences in vegetation composition and structure are inhabited by spider assemblages with negative scores on both DCA axes. Sites close to the mountain peak with patchy vegetation

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Journal Pre-proof dominated by grass species feature low species numbers and are characterized by spider assemblages with negative scores on DCA1 and positive scores on DCA2. Our results indicate spider species richness decreases along the elevational gradient. However, beta-diversity and thus spider assemblage composition are only to some degree explained by elevation, showing the highest values and an almost complete turnover in species between 1000 and 1500 m a.s.l.

4 | DISCUSSION

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4.1 | Methods The results of this study show that it is feasible to address the spatial patterns of spider species

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composition and diversity with predictive mapping approaches based on terrain and remotely

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sensed data. However, some methodological issues need to be considered when the resulting

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patterns are interpreted:

First, we used a pitfall trapping approach in this study to sample the field data. Pitfall trapping

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is a suitable and reliable method when one is interested in the diversity and abundance of epigeal

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arthropods, especially in heterogeneous arctic-alpine areas (Hauge, Hågvar, & Østbye, 1978; Finch & Löffler, 2010; Hein et al., 2014). The pitfall trap catches can display the activity abundance of a

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particular species and provide solid and reliable numbers for the total number of spider species present at certain sites (Uetz & Unziker, 1976; Topping & Sunderland, 1992). However, one has to keep in mind that pitfall trapping is biased towards ground-dwelling species and males due to their often higher activity while seeking mates (Merret & Snazell, 1983). The two most abundant families in our data were the Linyphiidae and Lycosidae, which is in concordance with several previous studies on spider diversity in alpine areas in Scandinavia (Hauge et al., 1978; Hauge & Refseth, 1979; Finch & Löffler, 2010). Consequently, it is not surprising that the four clusters revealed by the isopam analysis are characterized by species of these two families. Although the approach used for sampling covers a fine-scaled topographic gradient nested within the main elevational gradient, when interpreting the results, one has to be aware that these might be being

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Journal Pre-proof affected by spatial pseudo-replication, because true replicates are absent in our study (e.g. Oksanen, 2001). Likewise, spatial structures in the data may lead an inflated model fit due to the ubiquitous spatial autocorrelation inherent to most ecological field data. We did not take into account this spatial dependence in the validation process, and that this may lead to an overestimation of the model performance. It is important to note that for an implementation of such an approach in an applied context, future research is needed to confirm whether or not the proposed approach indeed can deliver satisfactory results.

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Second, for the spatially explicit predictor layers, we used topographical information derived

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from a DEM with a spatial resolution of approximately 30 m × 30 m as well as remotely sensed

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data with spatial resolutions of 10 m × 10 m and 20 m × 20 m. All spatial layers were resampled to

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a resolution of 10 m × 10 m. Although this resolution preserves the spatial variation in the visible and NIR bands of the Sentinel-2 data, some variation is obviously missing in the data sets with a

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lower resolution, most likely resulting in an underestimation of the heterogeneity in micro-

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topography and reflectance at the 10-m scale.

And last, whereas the models for DCA axis 1 and species richness could explain >60% of the

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variation in the response data, the model for DCA axis 2 explained only a minor percentage of the

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variation. This weak model fit is most likely due to the fact that predictors able to explain the trap distribution along this axis were missing in the analysis or only available with a resolution too coarse to capture important fine-scale variability. In our opinion, the variation along this axis is determined by the duration of snow cover at the respective sites. Whereas it is in generally possible to retrieve spatially explicit information on snow cover from a time series of optical remote-sensing data, the availability of image data for our study site was limited due to cloud cover, interfering with the retrieval of such spatially explicit snow-cover data. This weak model fit affects the spatial prediction ability of the DCA 2 scores and consequently leads to an underestimation of beta diversity. Thus the beta diversity for our study site is most likely higher, in particular for areas with large amounts of fine-scaled habitat heterogeneity.

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Journal Pre-proof 4.2 | Results In our study, vegetation indices derived from remote sensing were relatively powerful predictors for fine-scale spider composition. This agrees with previous studies on arthropod assemblages that have shown promising results using remote-sensing data to predict patterns in, for example, communities of spiders (Vierling et al., 2011; Lafage et al., 2014), beetles (Lassau & Hochuli, 2008; Müller & Brandl, 2009), ants (Lassau et al., 2005), and other herbivorous insects (Kemp, Linder, & Ellis, 2017). In our study, these vegetation indices represent the structural properties of the vegetation

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such as density, leaf area, and moisture that describe the habitat suitability and are sufficient for an assessment of the major changes in spider species composition. Besides these vegetation indices,

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the spectral bands in the visible and SWIR region had high importance values in the DCA 1 model.

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The reflectance values in these additional bands illustrate the differences in the habitats in the

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lichen-covered and unvegetated areas that are not expressed by the vegetation indices. Although the main changes along the first DCA axis are collinear with the elevation gradient, the DEM and

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derived topographical parameters were not considered important according to the variable

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importance measures of the DCA 1 model. This may be a result of their rather coarse resolution not covering the fine-scale microtopography present in our study area. The inclusion of additional biotic

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drivers may improve the predictions, especially at fine scales (Schweiger & Beierkuhnlein, 2016). Nevertheless, the DEM served as the main predictor for species richness, describing the dominant pattern in species richness distribution in the most parsimonious way. Due to the weak fit, the model for DCA 2 offers only a limited interpretability. The high importance of the visible bands indicates that the model mostly relies on the brightness differences between dwarf-shrub dominated vs. lichen-covered or unvegetated sites in the alpine parts of the study area. As discussed previously, we assume that fine-scale, spatio-temporally explicit information on snow cover may be able to considerably increase the model fit for DCA 2. However, this assumption cannot be tested as long as high resolution, cloud-free spring imagery of the study site is unavailable.

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Journal Pre-proof Decreasing species richness with increasing elevation is a general rule in studies focusing on ground-dwelling arthropods (Sanders & Rahbek, 2012). Bernadou, et al. (2015) found a clear decrease in ant species with increasing elevations in the Pyrenes, and Liu, et al. (2018) found a similar pattern in ant species richness in China`s Hengduan mountains. This linear decrease is probably a result of decreasing area with increasing elevation, which is accompanied by a decreasing number of habitats and an increasing conformity of the habitats present. This diversity pattern has been well documented as the species-area relationship by a large variety of studies (e.g.

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Connor & McCoy, 1979; Lomolino, 2000). Another well-documented explanation is the stressful

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climatic conditions at higher elevations such as the low temperatures (Hodkinson, 2005; Körner,

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2007; Gallou, et al., 2017). Additionally, the narrower the range of species with respect to their

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foraging strategy might allow them to outcompete each other when prey is less abundant (Schmitz, 2007). In this context, Quintero & Jetz (2018) found globally, that birds show rather a linear

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decrease in diversity along elevational gradients, then a mid-elevation peak, when corrected for

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species range. Our findings are inconsistent with recent theoretical predictions of a mid-elevation richness peak (Bertuzzo et al., 2016). However, this is probably a consequence of the relatively

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short extension (~600 m) of the elevational gradient in focus.

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Arthropod assemblages are determined by the structure and composition of the habitat and its complexity. In general, habitat heterogeneity and structure is one comprehensive factor influencing the diversity patterns of organisms (Rowe, 2009; Davies & Asner, 2014). Beta diversity is known to be positively correlated with environmental heterogeneity in terrestrial vertebrates (Ochoa-Ochoa, et al., 2014). For instance, spiders are especially bound to habitat structure at a local scale and show high dependencies on vertical structure and ground-cover (Schaefer, 1970; Frick, et al., 2007; Muff, et al., 2009). Spiders can be distinguished by two main foraging strategies: (1) web-building and (2) freerunning. Beyond that, several different strategies within the respective groups can be present, which results in a multitude of different foraging characteristics and requirements for the respective

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Journal Pre-proof habitats of the various families (Cardoso, et al., 2011). Low dispersal capacities, hunting manner and small distributional range of the species might be responsible for high beta-diversity values in heterogenous landscapes.

Our results for beta-diversity show clear distinctive patterns for spiders in the alpine environment in our research area. This highlights the pronounced habitat heterogeneity and associated arthropod assemblages at fine scales in arctic-alpine ecosystems. Whereas the lowest and

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highest elevated sites showed a low beta diversity, the mid-elevations were characterized by high

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beta diversity. This results in clear patterns of spider assemblages that are obviously related to the

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prevalent vegetation cover and the vertical vegetation structure. Relevant to this, previous studies

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have highlighted the importance of snow cover (Hein et al., 2014). The amount and duration of snow cover results in distinct patterns of vegetation composition and structure within our study

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area. Snow cover also plays a key role in determining the small-scale heterogeneity of these

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vegetation patterns in the arctic-alpine ecosystems of Scandinavia (Löffler, 2002, 2007; Odland & Munkejord, 2008). These distinctive patterns of snow cover and its duration thus influence spider

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composition both directly (e.g. season length, temperature, soil moisture) and indirectly (e.g.

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vegetation structure, open ground). Indirect effects include species occurring mainly in habitats on lee slopes, which are protected from cold temperatures during the winter by thick snow cover. Additionally, these habitats are characterized by relatively high vertical structure in comparison to the lower vertical vegetation structure at ridge sites, where species have to cope with less snow cover and thus lower temperatures during the winter. Nevertheless, the comparably lower vegetation structure might be favourable for highly mobile species. Temperature plays a crucial role for spiders in arctic and alpine environments, as they are considered rather ‘cold environments’. Foraging rates in spiders can be directly linked to higher temperatures (Ford, 1978; Høye & Forchhammer, 2008), but some species seem highly adaptable regarding their reproductive output in response to higher temperatures (Hein et al., 2018).

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Journal Pre-proof Additionally, direct effects via season length are often fitness related, with a prolonged season resulting in bigger specimens, which in turn results in increased offspring rates (Vertainen, et al., 2000; Høye et al., 2009). Consequently, both direct and indirect effects on spider fitness can lead to alterations in the reproduction of certain species, which might eventually lead to alterations in the composition of the spider assemblages in arctic and alpine environments (Ameline et al, 2018; Hein et al., 2018). The Norwegian mountain landscape shows the pronounced effects of extensive grazing over

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the past centuries (Rössler, et al., 2008). Previous studies have shown the influence of

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anthropogenic management measures on spider species richness in various landscapes (Pétillon &

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Garbutt, 2008; Pétillon, et al., 2014; Marín et al., 2016) as well as on spider assemblage

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composition (Pétillon & Garbutt, 2008; Noel & Finch, 2010). Our study provides an idea for a fast and inexpensive approach to detecting changes in arctic and alpine environments, which will lead to

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a better understanding of how human modifications (induced by changes in either the climate or

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land usage) might influence these heterogeneous ecosystems in the future. Thus, our approach helps

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5 | CONCLUSIONS

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to gain fundamental knowledge for sustainable management strategies.

Although no single key factor explained the spider assemblage patterns in the arctic-alpine ecosystems of Norway, we found that the diversity patterns of spiders can at least partially be modelled by means of remotely sensed data. Therefore, our approach proposes a sufficient new method for biodiversity assessments in arctic-alpine ecosystems using spiders as indicator species. Our approach will most likely profit from further advances in near-surface aerial photography and LiDAR systems. This should aid in developing better conservation strategies for the organisms within these threatened ecosystems.

ACKNOWLEDGEMENTS

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Journal Pre-proof We would like to thank Birgen Haest and one anonymous reviewer for constructive comments, which helped to improve the manuscript. We thank Christine Scholl & Yan Steil and our colleagues within the LTAER-Project in Norway for help with the fieldwork and Jan Kirsten for proof-reading the manuscript.

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FUNDING: The research received financial support from Color Line AS, Oslo.

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Journal Pre-proof Figure Legends FIGURE 1 Work flow of the analyses. DEM, digital elevation model. FIGURE 2

Bar plots of the number of (a) species and (b) specimens per family of the order

Araneae. FIGURE 3

Results of the detrended correspondence analysis (DCA). Trap sites with similar

species inventories are located at nearby positions, while trap sites with very dissimilar contents have positions at opposing ends of the ordination space. Symbols indicate the results (cluster #) of

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FIGURE 5 Importance of the predictor variables in the random forest regression models for DCA

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node impurity. In relative terms, a large increase in the node purity indicates a high importance of the respective variable. DCA, detrended correspondence analysis.

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FIGURE 6 Predicted spatial distribution of spider assemblages as expressed by the (a) DCA axes, (b) spider species richness as a measure of alpha diversity, and (c) spatial change rates of the DCA scores at the 90 m scale as a measure of beta diversity. Water bodies and shadows in the imagery are masked. The locations of the 40 trap sites are displayed in (a). DCA, detrended correspondence analysis.

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Highlights Mapping of arthropod alpha and beta diversity in heterogeneous arctic-alpine ecosystems

Spider diversity is a powerful biodiversity indicator in alpine ecosystems

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Can we model and map spider diversity using satellite images and elevation data?

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Elevation allows to predict spider species richness in alpine ecosystems

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Vegetation indices are powerful predictors of spider species composition

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From these maps we derive spatial patterns of spider beta diversity

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