{"paper_id":"3778b6d2-eba7-4139-978e-d4d6f8af94a3","body_text":"1 \n1 \nFennoTraits: Dataset of plant functional traits and community 1 \ncomposition in northern European flora 2 \n 3 \n 4 \nPekka Niittynen1 & Julia Kemppinen2,3 5 \n 6 \n 7 \nORCID 8 \nPekka Niittynen 0000-0002-7290-029X 9 \nJulia Kemppinen 0000-0001-7521-7229 10 \n 11 \nAffiliations 12 \n1 Water, Energy and Environmental Engineering research unit, University of Oulu, Oulu, 13 \nFinland 14 \n 15 \n2 Botany and Mycology Unit, Finnish Museum of Natural History, University of Helsinki, 16 \nHelsinki, Finland 17 \n 18 \n3 Department of Organismal and Evolutionary Biology, Faculty of Biological and 19 \nEnvironmental Sciences, University of Helsinki, Helsinki, Finland 20 \n 21 \nAuthor contributions 22 \nConceptualisation (equal), Data curation (equal), Formal analysis (PN), Funding acquisition 23 \n(equal), Investigation (equal), Methodology (equal), Project administration (equal), 24 \nResources (equal), Software (PN), Supervision (equal), Validation (PN), Visualization 25 \n(equal), Writing - original draft (equal), Writing - review & editing (equal) 26 \n 27 \nAcknowledgements 28 \nWe thank the personnel at the following research stations for their support during our 29 \nfieldwork and laboratory work: Kilpisjärvi Biological Station and Värriö Subarctic Research 30 \nStation, Oulanka Research Station, and Kevo Subarctic Research Institute. We thank the 31 \nUniversity of Helsinki, University of Oulu, and University of Turku, our work would not be 32 \npossible without these invaluable research stations across northern Finland. We thank Berit 33 \nTønsberg Gaski and Kari Anne Bråthen for their support during fieldwork at Máttavárri and 34 \ngiving us access to the Climate-ecological Observatory for Arctic Tundra cabin. We thank 35 \nTuuli Rissanen for sharing species lists of the Rásttigáisa study sites with us. We thank 36 \nJohanna Lehtinen and Miska Luoto for sharing species lists of the Pallas study sites with us. 37 \nWe thank Ian Brown from Stockholm University for helping to establish the Vindelfjällen 38 \nstudy design. We thank the 4th Plant Functional Trait Course held in Svalbard 2018, 39 \nparticularly Vigdis Vandvik, Aud H. Halbritter, Brian Maitner, and Brian J. Enquist who 40 \ntaught us how and why to sample plant functional traits. 41 \n 42 \nFunding 43 \nPN acknowledges funding from the Research Council of Finland (grant no. 378397; 347558; 44 \nPROFI8: 365202), Kone Foundation, and Nessling Foundation. JK acknowledges funding 45 \nfrom the Research Council of Finland (grant no. 349606; 353218; 370245) and the GeoDoc 46 \nprogramme at the University of Helsinki. 47 \n 48 \n 49 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n2 \n2 \nPermits 50 \nPermission to carry out fieldwork was granted by Metsähallitus. 51 \n 52 \nConflict of interest 53 \nThe authors have no conflict of interest. 54 \n 55 \n 56 \nAbstract 57 \nWe present here FennoTraits, which is a dataset of plant functional trait and community 58 \ncomposition data which we collected from Fennoscandia across northern Finland, Norway, and 59 \nSweden in 2016-2025. This dataset has 42 049 abundance estimations and 155 794 functional 60 \ntrait observations from 10 traits representing 373 vascular plant species collected from 1 235 61 \nstudy sites within seven study areas. The trait measurements consist of size -structural, leaf 62 \neconomic, leaf spectral, and reproductive traits. The species represent the majority of the native 63 \nvascular plant species that occur at the seven study areas, and many of the species occur in all 64 \nseven areas across the two biomes and their ecotone: tundra and boreal forests. Each study area 65 \nhas distinct characteristics and a range of habitats: tundra, meadows, wetlands, shrublands, and 66 \nboreal forests. These areas are under low anthropogenic influence, and many of the sites are 67 \nwithin protected areas that are reserved for nature conservation and scientific research. Finally, 68 \nwe provide with this dataset a general description of the main trait patterns and profiles of the 69 \nnorthern European flora. 70 \n  71 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n3 \n3 \nIntroduction 72 \nPlant functional traits can reveal the mechanisms driving ecosystem dynamics and species 73 \ninteractions. Functional traits are thus a powerful tool and have emerged into a critical research 74 \ntopic across ecology, biogeography, and environmental sciences (Violle et al. 2007, Diaz et al. 75 \n2016, Jennifer L. Funk et al. 2017). Plant functional traits are measurable plant characteristics 76 \nthat influence the growth, survival, and reproduction of plants, and also plant interactions with 77 \nthe environment and other organisms (Pérez -Harguindeguy et al. 2013). Therefore, it is 78 \nessential to understand how and why functional traits vary across and within species 79 \n(Kemppinen and Niittynen 2022, Laughlin 2024). Variation in functional traits can explain 80 \nhow plants adapt to changing environmental conditions, such as climate change, and how plants 81 \ncontribute to ecosystem services, such as carbon sequestration (Gerlinde B. De Deyn et al. 82 \n2008, Christiane Roscher et al. 2012, Georges Kunstler et al. 2016). Ultimately, functional 83 \ndiversity is a key component of biodiversity, contributing to ecosystem resilience and stability 84 \n(Cadotte et al. 2011, Mammola et al. 2021, Carmona et al. 2021). 85 \n 86 \nNorthern European ecosystems are facing rapid warming due to anthropogenic climate change, 87 \nwhich is challenging the resilience of these ecosystems and their provided services (Cohen et 88 \nal. 2014, Rantanen et al. 2022). Northern ecosystems play significant roles in carbon storage 89 \nregulation and nature-based livelihoods, and they also harbor unique biodiversity (Hobbie et 90 \nal. 2000, Ford et al. 2021). Northern Europe encompasses Finland, Sweden, and Norway, 91 \nrepresenting ecosystem diversity ranging from northern boreal forests to sub -Arctic and oro-92 \nArctic tundra. These ecosystems are characterised by strong environmental gradients due to 93 \ntheir rich geodiversity, providing mosaics of habitats and vegetation types that are adapted to 94 \ncold and wet climates (Wielgolaski 1975, Austrheim and Eriksson 2001, Kuuluvainen and 95 \nAakala 2011). The northern boreal forests are primarily composed of coniferous species 96 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n4 \n4 \nNorway spruce (Picea abies) and Scots pine ( Pinus sylvestris). Towards higher latitudes, the 97 \nconiferous forest shifts to sub -Arctic mountain birch forest ( Betula pubescens ssp. 98 \nczerepanovii). Towards higher altitudes, the deciduous forest transitions to dwarf -shrub 99 \ndominated oro -Arctic heath (chiefly, Empetrum nigrum  and Betula nana ), and finally, to 100 \nmostly barren mountain tops.  101 \n 102 \nMeasuring plant functional traits across wide environmental gradients is important for 103 \ninvestigating how plants are shaped by environmental change. Ultimately, functional trait data 104 \ncan be leveraged in establishing more effective conservation strategies and restoration efforts, 105 \nwhich requires extensive trait measurements (Carlucci et al. 2020). Some plant functional traits 106 \nare informative but laborious, and in turn, expensive to measure, such as root traits. Whereas, 107 \nother traits are more cost -efficient to collect, enabling replication of a high number of plant 108 \nspecies, communities, and study sites across large gradients. Such cost -efficient traits include 109 \nplant height, leaf area, specific leaf area (SLA), and leaf dry matter content (LDMC). These 110 \nare relatively fast and easy to measure, and require only a ruler, scale, scanner, and oven (Figure 111 \n1) (Pérez-Harguindeguy et al. 2013). Plant height and leaf area represent size -structural traits, 112 \nand SLA and LDMC represent leaf economic traits, and together these four traits form the two 113 \nprincipal trait variation axes globally, and are thus most often used in ecological research (Diaz 114 \net al. 2016). 115 \n 116 \nHere, we present FennoTraits, which is a taxonomically and spatially comprehensive dataset 117 \nof plant functional trait measurements in Fennoscandia across Finland, Norway, and Sweden 118 \n(Figure 1). We collected this dataset primarily from protected areas at seven study areas in 119 \n2016-2025. With this documentation of northern European flora, we aim to advance the 120 \nunderstanding of biodiversity, trait variability, and ecological responses across different 121 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n5 \n5 \nenvironmental conditions and large gradients. The dataset includes 42 049 abundance 122 \nestimations from 373 vascular plant species and 155 794 functional trait observations from 10 123 \ntraits representing 1 235 study sites, allowing analyses on intraspecific trait variability and its 124 \nresponses to a range of environmental gradients. The dataset consists of plant community 125 \ncomposition with a nested plot structure, enabling analyses on community structure, diversity, 126 \nand mechanisms linked to the locally measured plant traits. Lastly, this dataset is unique in the 127 \nsense that it was collected and processed only by two researchers, maximising data 128 \ncomparability across the many study designs and study areas. We followed the best practices 129 \nfor open and reproducible science in planning, collecting, documenting, and publishing this 130 \ndataset (Pérez-Harguindeguy et al. 2013, Hampton et al. 2015, Wilkinson et al. 2016, Jenkins 131 \net al. 2023). 132 \n 133 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n6 \n6 \n 134 \nFigure 1. Sampling protocol and study designs (a -f). We present a comprehensive dataset of 135 \nplant functional trait measurements that we collected from Fennoscandia across northern 136 \nFinland, Norway, and Sweden in 2016-2025. 137 \n 138 \nMethods 139 \nStudy areas 140 \nWe collected the data at seven study areas (Figure 1; Table 1; see Supporting information for 141 \nmaps Figures S1 -S7), four in Finland, two in Norway, and one in Sweden. We chose these 142 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n7 \n7 \nstudy areas due to their floristic diversity, accessibility and on -going research collaboration. 143 \nMany of the study areas are nearby research stations, facilitating both the field and laboratory 144 \nwork. 145 \n 146 \nMáttavárri area 147 \nThe Máttavárri area is located in northern Norway. In this low -Arctic area, the dominating 148 \nvegetation type is dwarf shrub tundra. The summits are bare. 149 \n 150 \nRásttigáisá area 151 \nThe Rásttigáisá area is located in northern Norway, close to the Norway-Finland border. In this 152 \nsub-Arctic area, the dominating vegetation type is dwarf shrub tundra. The summits are bare. 153 \n 154 \nKilpisjärvi area 155 \nThe Kilpisjärvi area is located in north -western Finland, partly extending to the Salloaivi 156 \nmountain close to the border of Finland and Norway. In this sub -Arctic area, the dominating 157 \nvegetation type is dwarf shrub tundra and mountain birch forest. The summits are bare. The 158 \narea is diverse and heterogeneous with both acidic and calcareous bedrocks. The area has 159 \nseveral conservation areas, including Malla Strict Nature Reserve and Saana Nature Reserve. 160 \n 161 \nPallas area 162 \nThe Pallas area is located in north -western Finland. In this hilly northern boreal area, the 163 \ndominating vegetation type is coniferous forest, which forms the tree line. The summits are 164 \ntundra heath vegetation. Aapa mires are common in the lowlands. The area is within the Pallas-165 \nYllästunturi National Park. 166 \n 167 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n8 \n8 \nVärriö area 168 \nThe Värriö area is located in north -eastern Finland, on the Finland -Russia border. In this 169 \nnorthern boreal area, the dominating vegetation type is coniferous forest, which nearly reaches 170 \nthe mountain tops. The summits are tundra heath vegetation. Aapa mires are common in the 171 \nlowlands. The area is within the Värriö Strict Nature Reserve.  172 \n 173 \nOulanka area 174 \nThe Oulanka area is located in eastern Finland, on the Finland -Russia border. In this boreal 175 \narea, the dominating vegetation type is mixed forest, which covers the entire area except the 176 \nopen wetlands and small patches of herb-rich deciduous forests. The area is within the Oulanka 177 \nNational Park.  178 \n 179 \nVindelfjällen area 180 \nThe Vindelfjällen area is located in northern Sweden, close to the Sweden -Norway border. In 181 \nthis mountain area, the dominating vegetation type is mountain tundra heath and extensive 182 \nherb-rich snowfields. The summits are bare. The area is within the Vindelfjäll Nature Reserve.  183 \n 184 \nTable 1.  Summary statistics of the study areas. N trait obs = number of unique trait 185 \nobservations; N species = number of unique species in data; N sites = number of unique study 186 \nsites with full plant community surveyed. 187 \nArea, \nacronym \nLat, Lon Size (km2) Elevation \n(m a.s.l.) \nSampling \nyears \nN trait obs N species N sites \nMáttavárri, \nMAT \n70.3; 29.1 50 196–468 2023 4 649 126 49 \nRásttigáisa, 70.0; 26.3 6 420–761 2023 1 988 47 - \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n9 \n9 \nRAS \nKilpisjärvi, \nKIL \n69.1; 20.8 160 477–1008 2016-2025 130 815 306 771 \nPallas, \nPAL \n68.0; 24.1 35 268–558 2024 1 365 54 - \nVärriö, \nVAR \n67.7; 29.6 30 262–475 2021 3 785 87 47 \nOulanka, \nOUL \n66.4; 29.3 100 143–384 2023-2025 10 849 200 103 \nVindelfjäll\nen, VIN \n65.8; 15.2 14 931–1408 2021-2023 2 343 73 43 \n 188 \nStudy designs 189 \nAt the seven study areas, we used different study designs, and in the Kilpisjärvi area we had 190 \n11 different study designs (Figure 1a-f; Table 2). Across all study areas and designs, the plant 191 \nfunctional trait and plant community composition data are fully comparable because we used 192 \nthe same methods and observers. Yet, each study design was established for a specific purpose, 193 \nand therefore, the spatial, environmental, and temporal structures differ among the areas and 194 \ndesigns. Where possible, we have provided references to the original study designs for detailed 195 \ninformation on the design and related environmental data. We have provided coordinates to the 196 \nentire dataset with GPS accuracy, and at most sites, we used a high-accuracy Global Navigation 197 \nSatellite System (chiefly, GeoExplorer GeoXH 6000 Series; Trimble Inc., Sunnyvale, CA, 198 \nUSA; or a comparable device) that provides up to centimeter-scale positioning accuracy. 199 \n 200 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n10 \n10 \nFor many of the study designs, we used random stratification to select the study sites. We used 201 \nthe random stratification to pre-select a set of candidate sites, maximising the coverage of the 202 \nmain environmental gradients within each study area. The variables that we used for stratifying 203 \nthe environmental space vary among the study areas and designs. We provide references for 204 \neach design when possible, and in short, the variables included, e.g., total canopy cover, 205 \ndeciduous canopy cover, distance to forest edge, altitude, potential incoming solar radiation, 206 \nand topographic wetness. 207 \n 208 \nAt many of the study designs, we used a nested plot structure (Figure 1a), unless we mention 209 \notherwise (Figure 1b -f). The nested plot structure means that at each site we had three plot 210 \nscales nested so that the centre of the plots align. The plot scales and forms are: 0.2 m x 0.2 m 211 \nsquared plot, 1.0 m x 1.0 m squared plot, and 2.0 m radius circular plot (Figure 1a).  212 \n 213 \nIn addition to the study designs (Figure 1a -f), we also collected extra leaf samples 214 \nopportunistically in the field (coded as “EXT” in the leaf trait data). We targeted these efforts 215 \nto gain more trait data for species that were underrepresented in the functional trait data 216 \ncollected at the studied plots. This means that the plant functional trait data also contains leaf 217 \ntrait measurements from these extra leaf samples, without accompanying height trait 218 \nmeasurements or the plant community composition data. 219 \n 220 \nThe study sites and plots are primarily unmanipulated, except for the leaf trait sampling, unless 221 \nwe mention otherwise, for instance, the Kilpisjärvi: Community experiment design. 222 \n 223 \nMáttavárri: Gradient design 224 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n11 \n11 \nIn the Máttavárri area, we sampled plant functional trait and plant community composition data 225 \nin 2023. This design c onsists of 49 study sites, and here, we used the nested plot structure  226 \n(Figure 1a). 227 \n 228 \nRásttigáisá: Gradient design 229 \nIn the Rásttigáisá area, we sampled plant functional trait data in 2023. This design c onsists of 230 \n49 study sites with 1 m x 1 m study plots (Figure 1b). These data consist only of leaf traits of 231 \nthe most abundant plant species in the plant communities. This means that the data do not 232 \ninclude height measurements or the plant community composition data. The original study 233 \ndesign is described in detail in Rissanen et al. (2023). 234 \n 235 \nKilpisjärvi: Woody plant design 236 \nIn the Kilpisjärvi area, we used a design focusing on woody plant species in tundra. In this 237 \ndesign, we measured woody species cover and height in 2016 -2017. This design consists of 238 \n223 study sites with five study plots, each 1 m x 1 m. In total, we had 1 053 plots which we 239 \nplaced hierarchically, so that at each site, one plot was at the centre of the site and four plots 240 \nwere placed in the four cardinal compass directions five meters from the centre plot (Figure 241 \n1c). These data consist of species-specific height measurements (median, maximum) and cover 242 \npercentages of woody plant species. This means that the data do not include other trait 243 \nmeasurements or the plant community composition data. The original study design is described 244 \nin detail in Kemppinen et al. (2021b) and Kemppinen et al. (2018). 245 \n 246 \nKilpisjärvi: Saana-Jehkas gradient design 247 \nIn the Kilpisjärvi area, we sampled plant functional trait and plant community composition data 248 \nin 2017-2025. This design consists of 228 study sites with 1 m x 1 m study plots. We have 249 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n12 \n12 \nstudied 50 of these sites more intensively, and used here the nested plot structure (Figure 1a) 250 \nand annual resampling of leaf traits. This design overlaps with the centre plots of the Kilpisjärvi 251 \nWoody plant design. The original study design is described in detail in Kemppinen et al. 252 \n(2021b) and Tyystjärvi et al. (2022). 253 \n 254 \nKilpisjärvi: Malla gradient design 255 \nIn the Kilpisjärvi area, we sampled plant functional traits and plant community composition in 256 \n2020-2025. This design consists of 70 study sites, and here, we used the nested plot structure 257 \n(Figure 1a). The original study design is described in detail in Aalto et al. (2022) and 258 \nKemppinen et al. (2023). 259 \n 260 \nKilpisjärvi: Rare Arctic design 261 \nIn the Kilpisjärvi area, we used a design focusing on rare Arctic vascular plant species and thus 262 \ntargeted their habitats, such as calcareous heaths and meadows. In this design, we sampled 263 \nplant functional trait and plant community composition data in 2020-2025. This design consists 264 \nof 182 study sites, and here, we used the nested plot structure (Figure 1a). 265 \n 266 \nKilpisjärvi: Ailakkavaara gradient design 267 \nIn the Kilpisjärvi area, we sampled plant functional trait and plant community composition data 268 \nin 2021-2025. This design consists of 41 study sites, and here, we used the nested plot structure 269 \n(Figure 1a). The original study design is described in detail in Aalto et al. (2022) and 270 \nKemppinen et al. (2023). 271 \n 272 \nKilpisjärvi: Arthropod sampling design 273 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n13 \n13 \nIn the Kilpisjärvi area, we used a design focusing on arthropods. In this design, we sampled 274 \nplant functional trait and plant community composition data in 2021. This gradient design 275 \nconsists of 35 study sites, and here, we used the nested plot structure (Figure 1a). The sites 276 \nwere used for sampling arthropods using malaise traps and ground traps, however, these were 277 \nnot placed in the plots. The original study design is described in detail in Peña -Aguilera et al. 278 \n(2023).  279 \n 280 \nKilpisjärvi: Microclimate grid design 281 \nIn the Kilpisjärvi area, we used a design focusing on within-species microclimate relationships 282 \nof six common tundra vascular plant species. In this design, we sampled plant functional trait 283 \ndata in 2021. This design consists of six study grids with 25 study plots, each 1 m x 1 m. In 284 \ntotal, we had 150 plots which we placed in a grid layout, so that in each grid, the 25 plots were 285 \nplaced at six m intervals (Figure 1d). These data consist only of plant functional traits of the 286 \nsix species. This means that the data do not include the plant community composition data. In 287 \ntwo plots, the focal species were not present and thus we measured traits from 148 plots. The 288 \noriginal study design is described in detail in Kemppinen & Niittynen (2022). 289 \n 290 \nKilpisjärvi: Spring design 291 \nIn the Kilpisjärvi area, we used a design focusing on springs. In this design, we sampled plant 292 \nfunctional trait and plant community composition data in 2022 -2023. This design consists of 293 \n32 study sites at or around springs, and here, we used the nested plot structure (Figure 1a). 294 \n 295 \nKilpisjärvi: Geodiversity gradient design 296 \nIn the Kilpisjärvi area, we used a design focusing on geodiversity. In this design, we sampled 297 \nplant functional trait and plant community composition data in 2023-2025. This design consists 298 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n14 \n14 \nof 39 study sites at locations with pronounced geomorphological processes (e.g., cryoturbation 299 \nand fluvial activity), and here, we used the nested plot structure (Figure 1a). 300 \n 301 \nKilpisjärvi: Community experiment design 302 \nIn the Kilpisjärvi area, we used a design with a plant community experiment. In this design, 303 \nwe sampled plant functional trait and plant community composition data in 2023, retrieving a 304 \nbaseline data prior to the community manipulations that followed in 2024 and 2025 when trait 305 \nsampling and community surveys were also repeated. This design consists of 24 replicated 306 \nstudy grids (each 2 m x 3 m) with six 1 m x 1 m study plots (Figure 1e). In total, we had 144 307 \nplots that represent herb-rich meadow vegetation. Within a grid, we had one control plot and 308 \nthe rest had a different manipulation: 1) species with the highest LDMC removed; 2) species 309 \nwith the lowest LDMC removed; 3) tallest species removed; 4) shortest species removed; 5) 310 \nspecies removed randomly. We conducted the manipulations in a given plot community by 311 \nactive removals (i.e., cutting the above -ground parts) so that at least half of the total vascular 312 \nplant cover was removed. The removals were repeated 2-3 times per growing-season. 313 \n 314 \nKilpisjärvi: Seasonal monitoring design 315 \nIn the Kilpisjärvi area, we used a design focusing on seasonality of intra-specific trait variation 316 \nof 11 common tundra vascular plant species. In this design, we sampled leaf functional trait 317 \ndata in 2024. This design consists of three study sites with 10 m -radius circular study plots 318 \n(Figure 1f). We conducted a weekly sampling for 15 weeks, the entire growing season. These 319 \ndata consist only of the leaf functional traits of the 11 species. This means that the data do not 320 \ninclude height measurements or the plant community composition data. The original study 321 \ndesign is described in detail in Niittynen et al. (2026). 322 \n 323 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n15 \n15 \nPallas: Gradient design 324 \nIn the Pallas area, we sampled plant functional trait data in 2024. This design consists of 23 325 \nstudy sites with 1 m x 1 m study plots (Figure 1b). These data consist only of leaf traits of the 326 \nmost abundant plant species in the plant communities. This means that the data do not include 327 \nheight measurements or the plant community composition data. The larger original study 328 \ndesign is described in detail in Lehtinen et al. (2025). 329 \n 330 \nVärriö: Gradient design 331 \nIn the Värriö area, we sampled plant functional trait and plant community composition data in 332 \n2021. This design consists of 47 study sites, and here, we used the nested plot structure (Figure 333 \n1a). The original study design is described in detail in Aalto et al. (2022) and Kemppinen et al. 334 \n(2023). 335 \n 336 \nOulanka: Gradient design 337 \nIn the Oulanka area, we sampled plant functional trait and plant community composition data 338 \nin 2023 -2025. This design consists of 103 study sites, and here, we used the nested plot 339 \nstructure (Figure 1a). 340 \n 341 \nVindelfjällen: Gradient design 342 \nIn the Vindelfjällen area, we sampled plant functional trait and plant community composition 343 \ndata in 2021-2023. This design consists of 43 study sites with 1 m x 1 m study plots (Figure 344 \n1b). 345 \n 346 \nTable 2. The data from the Kilpisjärvi study area originated from 11 different study designs. 347 \nN trait obs = number of unique trait observations; N species = number of unique species in 348 \ndata; N sites = number of unique study sites with trait or abundance data. 349 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n16 \n16 \nStudy design Acronym Sampling \nyears \nN trait \nobs. \nN species N sites \nWoody plant design KIL_WOO 2016-2017 9679 21 223 \nSaana-Jehkas gradient design KIL_MI 2017-2025 16084 141 228 \nMalla gradient design KIL_MAL 2020-2025 6966 136 70 \nRare Arctic design KIL_RA 2020-2025 23862 247 182 \nAilakkavaara gradient design KIL_AIL 2021-2025 4874 129 41 \nArthropod sampling design KIL_ROS 2021 4665 114 35 \nMicroclimate grid design KIL_ITV 2021 4107 6 148 \nSpring design KIL_L 2022-2023 4169 128 32 \nGeodiversity gradient design KIL_X 2023-2025 4451 141 39 \nCommunity experiment design KIL_EXP 2023-2025 48184 139 144 \nSeasonal monitoring design KIL_SEA 2024 1551 11 3 \n 350 \nTaxonomy 351 \nWe identified the plants to species level always when it was possible. There were only few 352 \nexceptions: 1) The genus Taraxacum is known for its complex taxonomy, and therefore, we 353 \nidentified it only to genus level; 2) Alchemilla species can be difficult to identify to species 354 \nlevel in field, and therefore, in some of our subdatasets it is only at genus level. An exception 355 \nto Alchemilla spp. is A. alpina, which we always identified at species level, because it is easy 356 \nto identify due to its separated leaflets. We used the Leipzig Plant Catalogue as the backbone 357 \nfor our taxon nomenclature. We harmonised the taxon names using the lcvplants R package 358 \n(Freiberg et al. 2020). Hybrids are common in Salix and Carex genera. When we suspected a 359 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n17 \n17 \nhybrid, we named the taxon after the most likely pair of species that formed the hybrid. In the 360 \ndataset, all hybrids are easy to separate using the provided taxonomic rank of each recorded 361 \ntaxon. 362 \n 363 \nPlant community composition data 364 \nWe collected the plant community composition data from the plots by identifying all vascular 365 \nplant species and visually estimating their species-specific cover percentages. The sum of cover 366 \nvalues within plots may exceed 100 %, as plants often overlap each other. 367 \n 368 \nIn the community data, there are four species (total of 10 occurrences in the plant community 369 \ncomposition data) that are classified as sensitive species in Finland. Therefore, we anonymised 370 \nthese species in the entire dataset (i.e., SpeciesA-D) to protect their precise locations in Finland, 371 \nfollowing the guidelines of the Finnish Biodiversity Information Facility (FinBIF). 372 \n 373 \nPlant functional trait data 374 \nWe collected the plant functional trait data by following the protocol outlined in Kemppinen 375 \n& Niittynen (2022) and Niittynen et al. (2026) which are based on the handbook for 376 \nstandardised measurements of plant functional traits (Cornelissen et al. 2003, Pérez -377 \nHarguindeguy et al. 2013). We collected data on 10 plant functional traits (Table 3), namely, 378 \nmedian height, maximum height, reproductive effort, fresh weight, dry weight, leaf area, SLA, 379 \nLDMC, leaf brightness index (BITM; Equation 1), and leaf greenness index (Excess Green 380 \nindex; ExG; Equation 2). The height traits are measured at plot -level, which means that a 381 \nspecies can have multiple height measures per site in those study designs in which we used the 382 \nnested plot structure. We measured the rest of the traits at species- and site-level, which means 383 \nthat we measured or sampled several individuals from each focal species and pooled the 384 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n18 \n18 \nsamples by site. Below, we explain our 9 -step plant functional trait protocol in chronological 385 \norder (Figure 1.1-9). 386 \n 387 \nFieldwork 388 \nWe measured plant height (Figure 1.1) with a ruler to centimeter precision (millimeter precision 389 \nfor the shortest plants), recording the median and maximum vegetative heights of the focal 390 \nspecies and excluding inflorescences. The median height is a visual estimation of the typical 391 \nheight of the canopy of a given species within the plot. In the nested plot structure, we measured 392 \nplant heights from the two smaller plots, but not from the largest circular plot size (Figure 1a). 393 \n 394 \nWe estimated reproductive effort (Figure 1.1) by documenting the sexual reproductive effort 395 \nof the focal species. We used a scale from 0 to 5, where 0 means that we did not observe any 396 \nsigns of sexual reproduction efforts at the study site (i.e., flowers, berries, fresh seed capsules), 397 \nand 5 means that all individuals showed signs of sexual reproduction efforts. If the individuals 398 \nwere large and hard to separate (e.g., Empetrum nigrum), 5 indicated exceptionally high density 399 \nof flowers or berries. Therefore, the reproductive effort should be used as an index that 400 \nindicates the relative intensity of sexual reproductive effort in a form that is comparable across 401 \nspecies and sites. We did not estimate reproductive effort for ferns. The reproductive effort 402 \ndata can also be missing in cases when we were not able to determine if sexual reproduction 403 \nwas present. For instance, if the plants were still developing or if the trees were too tall for us 404 \nto reach. 405 \n 406 \nWe collected leaf samples (Figure 1.2) from or near the plots using the plant community 407 \ncomposition data to determine which species to sample at each site. In general, we sampled the 408 \nentire community at a given site, except for species with a low coverage ≤ 1%. The exceptions 409 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n19 \n19 \nto this rule are: 1) all protected species, because we did not sample them in the countries in 410 \nwhich they were protected, and 2) specific study designs, where we did not collect any leaf 411 \nsamples (e.g., Kilpisjärvi: Woody plant design) or focused only on a set of focal species (e.g., 412 \nKilpisjärvi: Microclimate grid design). We selected fully opened and developed leaves (i.e., 413 \nnot curled) that showed no signs of damage, such as pathogens or herbivory. In general, we 414 \nsampled one leaf from 3-4 individuals of the focal species at a given study site at each sampling 415 \ntime point. The exceptions to this rule were species with very small leaves, such as Empetrum 416 \nnigrum. For those small -leaf species, we sampled 10 -15 leaves per individual and three 417 \nindividuals per given study site at each sampling time point. Regarding all species, we pooled 418 \nthe sampled leaves at the species and site level to reduce the workload. Regarding evergreen 419 \nshrub species, we selected leaves that were from previous years instead of new leaves that had 420 \nemerged during the sampling season. We transported the leaf samples (or branches of e.g., 421 \nEmpetrum nigrum) from the field to the laboratory in zip -lock bags with a drop of water to 422 \nkeep the leaves fresh or to rehydrate them. In the laboratory, we stored the sample bags at 4°C 423 \nand we processed the leaves within 48 hours. 424 \n 425 \nLaboratory work 426 \nWe determined fresh weight (Figure 1.3) by first preparing the leaves by removing the petioles, 427 \nand then, gently patting them dry from any excess water on their surfaces. Then, we weighed 428 \nthe leaves using a Mettler AE 100 scale (0.0001 g precision) or a comparable scale. 429 \n 430 \nWe scanned fresh leaves (Figure 1.4) right after weighing using a Canon CanoScan LiDE 400 431 \nscanner (600 dpi) or a comparable scanner. We imaged the adaxial leaf surface, i.e., the sun -432 \nfacing side of the leaves. Some leaves were too large to fit the scanner, so we chopped them 433 \nbefore scanning (e.g., Matteuccia struthiopteris). 434 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n20 \n20 \n 435 \nWe determined dry weight (Figure 1.5-6) by first preparing the leaves by drying them at 70°C 436 \nfor 48 hours using VWR VENTI -Line ovens. Then, we weighed the leaves right after drying 437 \nthem using a Mettler AE 100 scale (0.0001 g precision) or a comparable scale. 438 \n 439 \nLeaf segmentation 440 \nWe calculated leaf area (Figure 1.7 -8) using the leaf scans. We established the following 441 \nprocedure for leaf segmentation and shadow removal through an iterative optimisation process. 442 \nThe leaf segmentation method with accompanying computer code was published in Niittynen 443 \net al. (2026). This process involved applying the spectral index based rules and thresholds to a 444 \nlarge dataset of scans across multiple species, visually inspecting the outcomes, and then fine-445 \ntuning the used indices and threshold parameters until no significant errors or artifacts were 446 \nobserved. The final procedure goes as follows. First, we conducted an initial leaf segmentation 447 \nby applying thresholds to the blue and red channels, removing pixels in which the values of the 448 \nblue channel were >180 or the red channel <30. Next, we applied a Normalized Difference 449 \nYellowness Index (NDYI, Equation 3) threshold (< 0.13) at the image borders (20 pixels 450 \nclosest to the margins), excluding shadows that may occasionally appear on the image margins. 451 \nThen, we converted the filtered pixels into polygons, assuming that each polygon represented 452 \nan individual leaf. We discarded any polygons with an area <200 pixels to eliminate dirt on the 453 \nscans. We filled any small holes within the leaf polygons which are likely artifacts using the 454 \nfill_holes function from the smoothr R package (Strimas-Mackey 2025), with a threshold of 1 455 \n000 pixels. We also excluded polygons at the image borders (<50 pixels to the margins) to 456 \nremove possible shadows of the margins.  457 \n 458 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n21 \n21 \nNext, we used an iterative refinement process on individual leaf polygons, further reducing 459 \nshadows on the scans. Shadows often occurred around thick leaves, and the previous 460 \nprocedures were insufficient to exclude these shadows. So, first, we buffered each leaf polygon 461 \nwith 10 pixels, and then, we cropped and masked the original RGB image to this buffered area. 462 \nNext, we determined the position of the leaf, which we were able to determine based on the 463 \nscanner sensor geometry that systematically casts the shadows on the same side of the leaf 464 \nscans (towards the upper right corner). We leveraged this to determine the position of the leaf 465 \nand to estimate potential shadows in a specific direction based on that position. First, we 466 \ncalculated a centerline of the leaf using the centerline R package (Tsyplenkov 2025), involving 467 \nthe creation of a skeleton of the leaf polygon and tracing a path between two 'end points' defined 468 \nby the maximum and minimum X or Y coordinates (depending on the aspect ratio of the leaf). 469 \nThen, we were able to estimate the potential shadow locations relative to the orientation of the 470 \nleaf. Next, we calculated a distance raster to the centerline skeleton towards the direction where 471 \nshadows were possible. The shadows gradually shift from dark to light in a known direction, 472 \nso we calculated a focal Pearson correlation coefficient between the distance raster and the blue 473 \nchannel using a moving window of 15 x 15 pixels. Then, we could exclude a pixel as shadow 474 \nif the correlation coefficient was above 0.5, the Red Chromatic Coordinate index (RCC; 475 \nEquation 4) exceeded 0.2, and NDYI was less than 0.15. We considered pixels with a blue 476 \nchannel value greater than 200 as white background and excluded them. Then, we refined the 477 \nleaf margins with a focal majority filter (5x5 kernel). Finally, we used the processed RGB 478 \nimages from the leaf scans to quantify leaf area. 479 \n 480 \nWe calculated leaf brightness index (BITM, Equation 1) and leaf greenness index (ExG, 481 \nEquation 2; Figure 1.7 -8) using the leaf scans. We calculated colour indices that are used in 482 \nsatellite-based remote sensing of vegetation. These indices can be derived from red, green, and 483 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n22 \n22 \nblue wavelengths (Montero et al. 2023). We conducted all routine raster and vector processing 484 \nusing functions from the terra (Hijmans 2022) and sf (Pebesma 2018) R packages. 485 \nThe formulas for the spectral indices were as follows: 486 \nEquation 1: BITM = ((B² + G² + R²) / 3)^0.5 487 \nEquation 2: ExG = 2 * G - R - B 488 \nEquation 3: NDYI = (G - B) / (G + B) 489 \nEquation 4: RCC = R / (R + G + B) 490 \nwhere R represents the red, G the green, and B the blue channel in the scanned RGB images. 491 \n 492 \nFinally, we quantified specific leaf area (SLA; Figure 1.7 -8) by calculating the ratio between 493 \nleaf area and dry weight. We quantified leaf dry matter content (LDMC; Figure 1.7 -8) by 494 \ncalculating the ratio between dry weight and fresh weight. 495 \n 496 \nTable 3. Summary of the 10 plant functional traits and their prevalence in the data. 497 \nTrait Unit Explanation N trait obs. N species N sites \nmedian height cm Estimated median vegetative \nheight (inflorescence excluded) of \na focal species within a plot. \n27201 327 1157 \nmaximum \nheight \ncm Maximum vegetative height \n(inflorescence excluded) of a \nfocal species within a plot. \n27200 327 1157 \nreproductive \neffort \nunitless Intensity of the sexual \nreproduction effort of a species at \na study site. From 0 to 5, 0 \nindicating no signs of sexual \n18653 325 754 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n23 \n23 \nreproduction effort (flowers, \nberries, fruits), 3 indicating that \nhalf of the individuals show \nefforts of sexual reproduction, and \n5 indicating that all individuals \nshow efforts on sexual \nreproduction. \nfresh weight g Water-saturated fresh mass of the \nleaf \n11814 350 1039 \ndry weight g Dry mass of the oven-dried leaf 11805 350 1039 \nleaf area cm2 Area of the leaf derived from \nscanned leaf images taken of the \nfresh leaf \n11826 350 1040 \nspecific leaf \narea, SLA \nmm²/mg⁻¹ Specific leaf area (SLA) is the \nratio of dry weight and leaf area. \n11809 350 1039 \nleaf dry matter \ncontent, LDMC \ng/g Leaf dry-matter content (LDMC) \nis the oven-dry mass of \na leaf, divided by its water-\nsaturated fresh mass \n11814 350 1039 \nBrightness \nindex, BITM \nunitless An spectral index about the \nbrightness of the upper side of the \nleaf (Equation 1) \n11836 350 1040 \nExcess Green \nIndex, ExG \nunitless A spectral index indicating the \ngreenness of the upper side of the \nleaf (Equation 2) \n11836 350 1040 \n 498 \nData 499 \nWe provide here a general description of the main trait profiles of the northern European flora 500 \nand most frequent species in the dataset (Figure 2). 501 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n24 \n24 \n 502 \nFigure 2. Distributions of the nine continuous traits across the seven study areas (a). The most 503 \nfrequent species in the plant functional trait data colored by functional group (b). 504 \n 505 \nData and code availability 506 \nWe provide the dataset in the supporting information of this article. Up -to-date version of the 507 \ndataset will be maintained and openly available at a GitHub repository that we will link here 508 \nafter acceptance for publishing. Stable versions of the future dataset with Digital Object 509 \nIdentifiers (DOI) will be published in the Zenodo repository annually after major updates. 510 \n 511 \nDataset structure and data dictionary 512 \nWe provide a dataset that consists of five files: three data tables as text files, one metadata file 513 \nas OpenDocument Spreadsheet, and an R script. 514 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n25 \n25 \n 515 \n1) The file FennoTraits_community_heights.csv includes the plant community 516 \ncomposition data and plant height data (Table 4). These data are at plot-level. 517 \n2) The file FennoTraits_leaf_traits.csv includes the leaf trait data and reproductive effort 518 \ndata (Table 5). These data are at site-level. 519 \n3) The file FennoTraits_species.csv is the lookup table for the observed or sampled taxa 520 \nwith higher taxonomy and sampling summary statistics (Table 6). 521 \n4) The file FennoTraits_metadata.ods includes the common metadata in three 522 \nspreadsheets. This is the data dictionary of the full dataset, which includes the 523 \ninformation in Tables 4-6. 524 \n5) The file FennoTraits_combine_data.R is an R script to facilitate the use of the dataset. 525 \n 526 \nTable 4. Data dictionary for the plant community composition data and plant height data. 527 \nVariable Type Description Units / Values \nsite character Unique study site identifier, \nmatching site in the other files \n— \ndesign character Study design the observation \nbelongs to \n— \narea character Abbreviation of the study area — \nplot_type character Type of vegetation plot a = 20cm x 20cm; b = 1m x \n1m; c = circular plot with 2m \nradius \nfull_commu\nnity \nlogical Whether the record represents the \nfull plant community (TRUE) or \na focal taxon subset (FALSE) \nTRUE / FALSE \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n26 \n26 \ntreatment character Experimental treatment applied \nto the plot \nhigh_LDMC = species with \nthe highest LDMC removed; \ntall = the tallest species \nremoved; low_LDMC = \nspecies with the lowest \nLDMC removed; random = \nrandom subset of species \nremoved; short = the shortest \nspecies removed \ndate date Date of observation YYYY-MM-DD \nyear numeric Year of observation — \nLat numeric Latitude of the plot in decimal \ndegrees (WGS84) \n— \nLon numeric Longitude of the plot in decimal \ndegrees (WGS84) \n— \ncountry character Country where the observation \nwas made \nFIN = Finland; NOR = \nNorway; SWE = Sweden \ntaxon character Taxon name as used in the dataset \n(may be subspecies, species, \ngenus, or family) \n— \ncvr numeric Percentage cover of the taxon in \nthe plot \n% \nmedian_heig\nht \nnumeric Median vegetation height of the \ntaxon in the plot \ncm \nmax_height numeric Maximum vegetation height of \nthe taxon in the plot \ncm \n 528 \nTable 5. Data dictionary for the reproductive effort data and leaf trait data. 529 \nVariable Type Description Units / Values \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n27 \n27 \nsite character Unique study site identifier, matching \nsite in the other files \n— \ndesign character Study design the observation belongs to — \narea character Abbreviation of the study area — \ntreatment character Experimental treatment applied to the \nplot \nhigh_LDMC = species \nwith the highest \nLDMC removed; tall = \nthe tallest species \nremoved; low_LDMC \n= species with the \nlowest LDMC \nremoved; random = \nrandom subset of \nspecies removed; short \n= the shortest species \nremoved \ndate date Date of leaf sample collection YYYY-MM-DD \nyear numeric Year of sample collection — \nLat numeric Latitude of the observation in decimal \ndegrees (WGS84) \n— \nLon numeric Longitude of the observation in decimal \ndegrees (WGS84) \n— \ncountry character Country where the observation was \nmade \nFIN = Finland; NOR = \nNorway; SWE = \nSweden \ntaxon character Taxon name as used in the dataset (may \nbe subspecies, species, genus, or \nfamily) \n— \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n28 \n28 \nreproduction numeric Sexual reproductive effort index of the \nspecies at site at year of sampling \n— \nn_inds numeric Number of individuals sampled (and \npooled) for leaf traits \n— \nn_leaf numeric Total (sum) of leaves sampled, pooled \nand processed for leaf traits \n— \nleaf_area numeric One-sided leaf area cm² \nSLA numeric Specific leaf area (leaf area per unit dry \nmass) \nmm² mg⁻¹ \nLDMC numeric Leaf dry matter content (dry mass per \nunit fresh mass) \ng g⁻¹ \nw_weight numeric Fresh (wet) mass of a leaf g \nd_weight numeric Dry mass of a leaf after drying g \nBITM numeric Brightness index of upper side of a \nplant leaves derived from scanned RGB \nimagery; formula = ((B² + G² + R²) / \n3)^0.5 \ndimensionless \nExG numeric Excess Green Index of upper side of a \nplant leaves from scanned RGB \nimagery; formula = (2G − R − B) \ndimensionless \n 530 \nTable 6. Data dictionary for the taxonomic lookup table of the observed or sampled taxa. 531 \nVariable Type Description Units / Values \ntaxon character Taxon name as used across all dataset files; \nprimary key for joining to the other files \n— \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n29 \n29 \nspecies character Accepted species name under the LCVP \nbackbone taxonomy \n— \nauthor character Author(s) of the accepted species name — \ngenus character Genus — \nfamily character Family — \norder character Order — \nrank character Taxonomic rank of the taxon entry sub-species / \nspecies / genus / \nfamily / hybrid \nn_areas numeric Number of distinct study areas in which the \ntaxon was recorded \n— \nn_locations numeric Number of distinct plot locations at which \nthe taxon was recorded \n— \nn_leaf_samples numeric Number of individual leaf trait \nmeasurements available for the taxon \n— \n 532 \nTechnical validation 533 \nWe conducted a systematic quality control procedure on all plant community composition and 534 \nplant functional trait observations. In the procedure, we combined visual inspection, 535 \nbiologically motivated hard thresholds, and within -species trait covariance analysis. We 536 \ndesigned the procedure to be conservative: instead of removing observations outright based on 537 \nstatistical criteria alone, each step aimed to identify the specific measurement most likely to be 538 \nerroneous before we set any values as missing. 539 \n 540 \nFirst, we produced two sets of diagnostic figures for each species with sufficient data: 1) Per -541 \nspecies histograms of all trait distributions, allowing visual identification of extreme isolated 542 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n30 \n30 \nvalues and measurement artefacts, and then 2) Pairwise trait-trait scatterplots with fitted linear 543 \ntrends for all ecologically meaningful trait pairs: log(SLA) against LDMC, log(d_weight) 544 \nagainst log(w_weight), log(leaf_area) against log(w_weight), log(leaf_area) against 545 \nlog(d_weight), ExG against BITM, and log(max_height) against log(median_height). We used 546 \nthese plots and species -level summary statistics to identify any suspicious trait observations, 547 \nwhich we then manually double -checked using our field notes and raw data, and finally, we 548 \ncorrected those observations if we found a clear source of error, such as a typing error. 549 \n 550 \nAfter the visual inspection, we applied a set of hard bounds derived from the known biological 551 \nand physical constraints of each measured variable. We flagged observations with the 552 \nfollowing criteria: 1) Cover values outside the range 0.25 –100%; 2) Plant height values <0 or 553 \n>3000 cm; 3) Observations in the plant community composition data, where the maximum 554 \nheight was less than the median height; 4) LDMC values at or outside the bounded interval 555 \n0−1; 5) SLA values below <0 or >200 mm²/mg⁻¹; 6) Leaf area, fresh mass, and dry mass values 556 \nof ≤0; 7) Observations in the plant functional trait data, where dry mass exceeded fresh mass; 557 \n8) Image-derived colour indices we checked against their theoretical ranges: −1-1 for ExG and 558 \n0-1 for BITM. Based on these criteria, we inspected all flagged observations and set the traits 559 \nto missing when we confirmed that there were errors or if we were not able to locate and correct 560 \nthe source of the error. 561 \n 562 \nWhen inspecting the data, we noticed that a simple statistical outlier detection based on 563 \nunivariate methods is poorly suited to our ecological trait data collected along wide 564 \nenvironmental gradients. This is because the extreme values often reflect genuine biological 565 \nvariations, and also, because the distributions are typically right -skewed and highly species -566 \nspecific. We therefore based the main automated detection procedure on the within -species 567 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n31 \n31 \ncovariance structure of functionally related trait pairs. This approach rests on the principle that 568 \nmost of the traits are not independent: leaf wet and dry weight share a near-constant ratio within 569 \na species, and leaf area scales predictably with both wet and dry weight. Consequently, their 570 \nderivatives (.ie., LDMC and SLA) are also typically highly correlated. Severe single -571 \nobservation deviations from these expected relationships within a species are therefore more 572 \nindicative of measurement error than of true biological variation. 573 \n 574 \nNext, we focused on four trait pairs: log(d_weight) against log(w_weight), log(leaf_area) 575 \nagainst log(d_weight), log(leaf_area) against log(w_weight), and log(SLA) against LDMC. 576 \nFor these pairs, we fitted a robust linear regression per species using Huber M -estimation as 577 \nimplemented in the rlm function of the MASS package (Venables and Ripley 2002). Here, we 578 \npreferred robust regression over ordinary least squares because the latter is sensitive to the very 579 \noutliers we seek to detect: a single erroneous observation can pull the fitted line toward itself, 580 \nreducing its own residual and evading detection. The Huber estimator down -weights 581 \nobservations with large residuals during fitting, so the resulting line reflects the central 582 \ntendency of the majority of observations and outlying points to receive appropriately large 583 \nresiduals. Because all continuous trait distributions were right -skewed and spanning several 584 \norders of magnitude, prior to fitting, we applied log-transformation for all pairs except LDMC 585 \nagainst SLA, where LDMC was left on its original scale as a bounded near-symmetric variable. 586 \nHere, we included only species with ≥10 complete observations for a given pair. We 587 \nstudentised residuals from each robust fit by dividing them with their median absolute deviation 588 \n(MAD), yielding a dimensionless measure of how many MADs each observation lies from the 589 \nspecies-specific regression trend. Then, we retained this MAD -studentised residual as a 590 \ncontinuous diagnostic variable for each trait pair. 591 \n 592 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n32 \n32 \nSince the three weight and leaf area pairs share raw trait measurements (i.e., leaf area, wet 593 \nweight, dry weight), we could use the pattern of high residuals across the pairs to identify which 594 \nspecific raw measurement is most likely in error. This is because each of the three raw trait 595 \nmeasurements is absent from exactly one of the three pairs: leaf area does not appear in the 596 \ndry-against-fresh-weight pair, dry weight does not appear in the area-against-fresh-weight pair, 597 \nand fresh weight does not appear in the area -against-dry-weight pair. Consequently, if a 598 \nmeasurement was erroneous, the one pair that did not involve it would show a low residual 599 \nwhile the two pairs that did involve it would show high residuals. We used this logic to assign 600 \neach observation a suspect variable: observations that had a high residual in the leaf area pairs 601 \nbut a low residual in the weight pair indicated a suspicious leaf area; observations that had a 602 \nhigh residual in the dry-weight pairs combined with a low residual in the wet-weight-area pair 603 \nindicated a suspicious dry weight; and the complementary pattern indicated a suspicious fresh 604 \nweight. If observations did not fit any of these three clean patterns, we classified them as 605 \nambiguous and we inspected them manually. We used the SLA–LDMC pair as a confirmatory 606 \nsignal rather than a diagnostic one, since either or both of these derived traits are affected if 607 \nany of the raw trait measurements is erroneous. We subjected to diagnosis observations with a 608 \nMAD residual >5 in any of the three raw trait measurement pairs, and subsequently, we set the 609 \ntraits to missing according to the identified suspect variable and its downstream consequences 610 \nfor derived traits: a suspicious leaf area propagated to missing SLA, BITM, and ExG; a 611 \nsuspicious fresh weight propagated to missing LDMC; and a suspicious dry weight propagated 612 \nto missing SLA and LDMC. Additionally, we observations to missing if they had a MAD 613 \nresidual >8 in the SLA–LDMC pair that had not been resolved by the pattern-based diagnosis, 614 \nand we did this across all derived trait measurements and raw trait measurements, as deviations 615 \nof this magnitude indicated a measurement inconsistency too severe to attribute to a single 616 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n33 \n33 \nsource. In total, we determined 14 leaf samples containing clearly erroneous trait values and 617 \nwe set their particular traits to missing. 618 \n 619 \nUsage notes 620 \nData use and best practice notes 621 \nWe provide this dataset under a CC-BY license. We recommend users of the dataset to cite this 622 \ndata article when referencing and using these data. We encourage contact from users for 623 \nguidance, advice, and collaboration. We appreciate users contacting us before visiting the study 624 \nsites due to our long-term monitoring programs. 625 \n 626 \nStrengths 627 \nA major strength of this dataset is the high level of operator consistency. This means that all 628 \nfield and laboratory work was conducted by the same two researchers. Therefore, we were able 629 \nto limit subjectivity in this dataset, reducing errors that may arise from observer bias and 630 \nmaximising comparability across study designs and study areas. 631 \n 632 \nA second major strength is the nested plot structure that we used in many of the study designs, 633 \nenabling multi -level analyses. However, this structure also requires careful consideration, 634 \nbecause the trait measurements represent different levels of observation. This means that we 635 \ndocumented the plant community composition and plant height at the plot -level, whereas, we 636 \nmeasured leaf traits and reproductive effort at the site-level. 637 \n 638 \nFinally, a third key strength is the high spatial accuracy of the recorded study site coordinates. 639 \nWe provide coordinates up to centimetre -scale positioning accuracy, ensuring that the exact 640 \nsame study sites can be reliably relocated in the future, and that the spatial context of the 641 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n34 \n34 \nobservations incorporated into analyses. High positioning accuracy also enabled us to do true 642 \nresurveys of the exact same sites, plots, and plant communities in the Kilpisjärvi Saana-Jehkas 643 \ngradient design, Kilpisjärvi Community experiment design, and Kilpisjärvi Seasonal 644 \nmonitoring design. 645 \n 646 \nConsiderations 647 \nTemporal context is one the most important factors to consider when using this dataset. We 648 \ncollected the data over more than a decade. This means that different parts of the dataset 649 \noriginate from different years, both within and among study areas and study designs. During 650 \nour observation and sampling period 2016 –2025, the climatic conditions in northern Europe 651 \nwere extreme at times, including winter warming and snow -on-ice events (Aalto et al. 2026). 652 \nMoreover, 2024 was likely the warmest growing season in northern Europe in the past 2000 653 \nyears (Rantanen et al. 2025). 654 \n 655 \nSeasonal dynamics should also be considered. We collected the data during peak growing 656 \nseasons, except in the Kilpisjärvi Seasonal monitoring design, in which we documented 657 \nseasonal patterns in leaf traits, see Niittynen et al. (2026). However, growing season dynamics 658 \ncan vary substantially in northern ecosystems. For instance, the onset of the growing season 659 \nmay shift between years and vary across local environmental gradients (Rantanen et al. 2026). 660 \n 661 \nLand use practices and particularly grazing pressure contribute to variation among study areas 662 \nand study sites. For example, the study sites in Kilpisjärvi study area are located within three 663 \ndistinct reindeer pastures. Thus, the grazing pressure by the semi -domesticated reindeer 664 \n(Rangifer tarandus tarandus ) and its spatiotemporal dynamics vary greatly even within the 665 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n35 \n35 \nstudy area. Reindeer grazing affects the plant community composition and likely also plant 666 \nheights (Olofsson et al. 2009, Maliniemi et al. 2018, Happonen et al. 2019). 667 \n 668 \nThe dataset captures a wide range of environmental variation within study areas, because we 669 \nselected the study sites within each study area using stratified random sampling. However, this 670 \ndataset should not be considered an unbiased representation of entire landscapes, and this 671 \nshould be considered especially when calculating species -level average trait values. For 672 \nexample, atypical habitats are overrepresented relative to their true frequency, such as springs 673 \nin the Kilpisjärvi Spring design and geofeatures in the Kilpisjärvi Geodiversity gradient design. 674 \n 675 \nFurthermore, we measured vegetative height excluding inflorescences. However, plant height 676 \nmeasurements are dependent on the presence of inflorescence in many species. In our study 677 \nareas, species such as Solidago virgaurea often occurred only with the leaf rosettes close to the 678 \nground, but their height measurements were considerably taller, if they produced flowers at the 679 \ntop of a tall shoot with many leaves. 680 \n 681 \nRegarding leaf traits, we also measured the leaf traits of horsetails (Equisetum spp.). However, 682 \nusing these data calls for consideration because these species do not really have leaves. 683 \nTherefore, we used the smaller branches as equivalents of leaves for some species, such as E. 684 \nsylvaticum. Whereas, for the non -brancing species (E. scirpoides, E. variegatum, E. hyemale , 685 \nand E. fluviatile), we calculated the leaf traits using the whole green shoot. 686 \n 687 \nFinally, we emphasise that the plant community composition data and the plant height data 688 \nrepresents the entire plant community of each site, whereas the leaf trait data does not. This is 689 \nbecause we collected leaf samples of the entire community at each site, except for species with 690 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n36 \n36 \na low coverage ≤ 1%. More importantly, we did not sample any protected species, in the 691 \ncountries in which they were protected. Therefore, the leaf trait data do not represent the entire 692 \nplant community. 693 \n 694 \nLimitations and uncertainties 695 \nRegarding taxonomy, we consider the main source of uncertainty to be species 696 \nmisidentification. We mitigated this risk through extensive prior experience in species 697 \nidentification of these northern ecosystems (Niittynen et al. 2018, Kemppinen et al. 2021a). In 698 \naddition, all leaf sample identifications were verified by both of us during laboratory work. 699 \nNevertheless, we acknowledge that hybridisation is common in certain groups, such as Salix 700 \nspp. and Carex spp., and such cases we have explicitly marked as hybrids in the dataset. 701 \n 702 \nRegarding the plant community composition data, we consider the main limitation to be the 703 \nvisual estimation that we used for estimating the species coverages. We minimised observer 704 \nbias because all estimates were made by the same two researchers, and we cross-calibrated our 705 \nestimations. However, visual estimation is inherently more subjective than, for instance, image-706 \nbased analysis or the point-intercept method. As a result, direct comparisons of species cover 707 \nwith future resurveys should be conducted with particular care. 708 \n 709 \nRegarding plant height, we consider the main source of uncertainty to be the median height 710 \nwhich we visually estimated. This means that we first visually inspected the representative 711 \nmedian height of the given species in the plot, and then based on this inspection, we measured 712 \nan individual representative of median height. We chose this approach to reduce workload, but 713 \na more robust approach would have involved measuring a larger number of individuals and 714 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n37 \n37 \nthen calculating the median based on their measured heights. Consequently, additional 715 \nmeasurements would be required to obtain more objective estimates of median height. 716 \n 717 \nFinally, regarding leaf trait data, we consider the main limitation to be that we pooled the leaf 718 \nsamples at site-level. We chose this approach to reduce workload, but a more comprehensive 719 \napproach would have involved conducting the leaf trait measurements at plot -level and 720 \nindividual-level. Our approach limits the ability to quantify variation within -plot and within-721 \npopulation. Consequently, more detailed sampling at finer scales and sampling more local 722 \nreplicates would be required to reduce potential noise arising from inter -individual and intra-723 \nindividual variation which affects the trait averages at plot-level (Maitner et al. 2023). 724 \n  725 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n38 \n38 \nSupporting information 726 \n 727 \nFigure S1. Map of the Máttavárri study area. Colours represent elevation (m a.s.l.) overlaid 728 \nwith hillshade. Black dots represent the study site locations. Water bodies in white. The 729 \ncoordinate system is UTM34N WGS84. 730 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n39 \n39 \n 731 \nFigure S2. Map of the Rásttigáisá study area. Colours represent elevation (m a.s.l.) overlaid 732 \nwith hillshade. Black dots represent the study site locations. Water bodies in white. The 733 \ncoordinate system is UTM34N WGS84. 734 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n40 \n40 \n 735 \nFigure S3. Map of the Kilpisjärvi study area. Colours represent elevation (m a.s.l.) overlaid 736 \nwith hillshade. Black dots represent the study site locations. Water bodies in white. The 737 \ncoordinate system is UTM34N WGS84. 738 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n41 \n41 \n 739 \nFigure S4. Map of the Pallas study area. Colours represent elevation (m a.s.l.) overlaid with 740 \nhillshade. Black dots represent the study site locations. Water bodies in white. The coordinate 741 \nsystem is UTM34N WGS84. 742 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n42 \n42 \n 743 \nFigure S5. Map of the Värriö study area. Colours represent elevation (m a.s.l.) overlaid with 744 \nhillshade. Black dots represent the study site locations. Water bodies in white. The coordinate 745 \nsystem is UTM34N WGS84. 746 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n43 \n43 \n 747 \nFigure S6. Map of the Oulanka study area. Colours represent elevation (m a.s.l.) overlaid with 748 \nhillshade. Black dots represent the study site locations. Water bodies in white. The coordinate 749 \nsystem is UTM34N WGS84. 750 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 9, 2026. ; https://doi.org/10.64898/2026.04.07.716889doi: bioRxiv preprint \n\n44 \n44 \n 751 \nFigure S7. Map of the Vindelfjällen study area. Colours represent elevation (m a.s.l.) overlaid 752 \nwith hillshade. Black dots represent the study site locations. Water bodies in white. 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