DOI: 10.5061/dryad.2547d7x1f
title: Data from: Resource quantity and heterogeneity drive successional plant diversity in managed and unmanaged boreal forests

description: Here, we describe the data and methods supporting a study on how
 disturbances such as forest fires and clear-cutting influence understory
 vegetation diversity in boreal Scots pine (Pinus sylvestris) forests in
 Northern Sweden. The research contrasts two chronosequences, one under
 rotational management (clear-cutting, soil scarification, and thinning)
 and one unmanaged and fire-origin, to examine how resource availability
 and heterogeneity relate to alpha and beta diversity over a temporal
 gradient. The understory vegetation of boreal forests plays a crucial role
 in maintaining biodiversity by creating habitats, supplying food
 resources, and regulating microclimate and soil conditions. This essential
 layer is frequently affected by disturbances such as forest fires and
 clear-cutting, which significantly alter understory communities and the
 ecosystem resource availability and heterogeneity. This study aimed to
 understand how these disturbances influence the spatial and temporal
 dynamics of key ecosystem resources, and subsequently the patterns of
 understory diversity. We analyzed and compared understory vegetation
 diversity in a rotational management chronosequence and an unmanaged fire
 chronosequence of Scots pine (Pinus sylvestris) forests across Northern
 Sweden. We assessed the relationship of above- and belowground resource
 availability and heterogeneity with alpha and beta diversity using
 generalized additive models and multivariate analyses. We found that
 belowground resource availability (especially inorganic nitrogen) and
 aboveground resource heterogeneity (especially variation in forest
 structural complexity) were most strongly positively correlated with alpha
 and beta diversity, varying across successional stages. In early stages
 (0-60 years), high availability of belowground resources and aboveground
 heterogeneity was associated with high alpha and beta diversity. In mid
 stages (100-200 years), reduced belowground resource availability and
 aboveground heterogeneity was linked to lower diversity. In late stages
 (&gt;250 years, which only exists in the Unmanaged Fire
 chronosequence), increased aboveground heterogeneity associated with tree
 mortality was linked to a resurgence in alpha and beta diversity. These
 results highlight the necessity of maintaining a mosaic of stands with
 different disturbance regimes and successional stages, particularly early
 post-fire stands and late successional stands, which are currently much
 rarer on the landscape, to support biodiversity at the landscape level.
description: # Data from: Resource quantity and heterogeneity drive successional plant
 diversity in managed and unmanaged boreal forests
 [https://doi.org/10.5061/dryad.2547d7x1f](https://doi.org/10.5061/dryad.2547d7x1f) **Journal: Ecography** **DOI: 10.1111/ecog.07676** ## Description of the data and file structure This README describes the data and methods supporting a study on how disturbances such as forest fires and clear-cutting influence understory vegetation diversity in boreal Scots pine (Pinus sylvestris) forests in Northern Sweden. The research contrasts two chronosequences, one under rotational management (clear-cutting, soil scarification, and thinning) and one unmanaged and fire-origin, to examine how resource availability and heterogeneity relate to alpha and beta diversity over a temporal gradient. ### **00 All Data** All datasets used for analyses are provided in the 00 All Data folder, where each row represents a stand (site) and each column represents either an environmental variable or a derived coefficient of variation. Please note that the data tables are redundant; yet for easier use they are contained in the same folder as the corresponding R scripts. "All data plot level" contains all variables measured at a plot (stand) level and therefore contains 36 rows. Variables in "All data plot level" (columns): * Site (this is the name of the stand) * Time (time since last disturbance, in years) * Management (2 levels: M for rotational management, F for unmanaged fire) * LAI (leaf area index; dimensionless; represents the one-sided green leaf area per unit of ground area of the forest canopy) * LAI_normalized (normalized LAI by way of min-max normalization; dimensionless) * LAI_cv_normalized (coefficient of variation of the normalized LAI; dimensionless) * Db_value (measure for forest structural complexity; dimensionless) * Db_value_cv (coefficient of variation of Db_value; dimensionless) * pH (soil pH; measured using a 1:5 soil-to-water ratio in samples taken from the Oa horizon) * moisture (taken from SLU's soil moisture map; dimensionless) Soil nutrients (for measurements and units please refer to 04 Belowground Resources) * Ntot (total inorganic nitrogen) * NO3 (nitrate) * NH4 (ammonium) * Al (Aluminum) * B (Bor) * Ca (Calcium) * Cu (Copper) * Fe (Iron) * K (Potassium) * Mg (Magnesium) * Mn (Manganese) * Na (Sodium) * P (Phosphorus) * S (Sulfur) * Zn (Zinc) * pH_cv (coefficient of variation of pH; dimensionless) * moisture_cv (coefficient of variation of moisture; dimensionless) * LAI_cv (coefficient of variation of LAI; dimensionless) * Ntot_cv (coefficient of variation of Ntot; dimensionless) * NO3_cv (coefficient of variation of NO3; dimensionless) * NH4_cv (coefficient of variation of NH4; dimensionless) * Al_cv (coefficient of variation of Al; dimensionless) * B_cv (coefficient of variation of B; dimensionless) * Ca_cv (coefficient of variation of Ca; dimensionless) * Cu_cv (coefficient of variation of Cu; dimensionless) * Fe_cv (coefficient of variation of Fe; dimensionless) * K_cv (coefficient of variation of K; dimensionless) * Mg_cv (coefficient of variation of Mg; dimensionless) * Mn_cv (coefficient of variation of Mn; dimensionless) * Na_cv (coefficient of variation of Na; dimensionless) * P_cv (coefficient of variation of P; dimensionless) * S_cv (coefficient of variation of S; dimensionless) * Zn_cv (coefficient of variation of Zn; dimensionless) * AverageDissimilarity (average within-stand dissimilarity between vegetation subplots; dimensionless) * ARA (Aboveground Resource Availability; dimensionless) * ARH (Aboveground Resource Heterogeneity, dimensionless) * BRA (Belowground Resource Availability; dimensionless) * BRH (Belowground Resource Heterogeneity, dimensionless) "All data subplot level" contains all variables measured at a subplot level and therefore contains 36 x 20 = 720 rows. * Stand (this is the name of the stand) * Subplot (in "All data subplot level" only; this is the number of the subplot from 1 to 20) * Time (time since last disturbance, in years) * Management (2 levels: M for rotational management, F for unmanaged fire) * humus_depth (depth of the humus layer; measured in cm) * stoniness (penetration depth of a metal rod; measured in cm) * pH (soil pH; measured using a 1:5 soil-to-water ratio in samples taken from the Oa horizon) * moisture (taken from SLU's soil moisture map; dimensionless) * LAI (leaf area index; dimensionless; represents the one-sided green leaf area per unit of ground area of the forest canopy) * Db_value (measure for forest structural complexity; dimensionless) Soil nutrients (for measurements and units please refer to 04 Belowground Resources) * Ntot (total inorganic nitrogen) * NO3 (nitrate) * NH4 (ammonium) * Al (Aluminum) * B (Bor) * Ca (Calcium) * Cu (Copper) * Fe (Iron) * K (Potassium) * Mg (Magnesium) * Mn (Manganese) * Na (Sodium) * P (Phosphorus) * S (Sulfur) * Zn (Zinc) Species (the following columns represent vascular plant, bryophyte, and lichen species; the numbers indicate hits per square meter. For information on measurement please refer to 02 Vegetation Metrics): * Myrtillus (Vaccinium myrtillus) * Vitis (Vaccinium vitis-idaea) * Uliginosum (Vaccinium uliginosum) * Empetrum (Empetrum nigrum) * Linnea (Linnaea borealis) * Ledum (Rhododendron tomentosum) * Calluna (Calluna vulgaris) * Huperzia (Huperzia selago) * Annotinum (Lycopodium annotinum) * Complanatum (Lycopodium complanatum) * Lagopus (Plantago lagopus) * Dryopteris (Dryopteris spp.) * Avenella (Avenella flexuosa) * Pilosa (Luzula pilosa) * Globularis (Carex globularis) * Carex sp. (Carex spp.) * Canescens (Carex canescens) * Mellampirum (Melampyrum sylvaticum) * Solidago (Solidago virgaurea) * Pirula (Pyrola secunda) * Epilobium (Chamerion angustifolium) * Rumex (Rumex acetosella) * Trientalis (Trientalis europaea) * Geranium (Geranium sylvaticum) * Chamaemorus (Rubus chamaemorus) * Arcticus (Rubus arcticus) * Saxatilus (Saxifraga spp.) * Hieracium (Hieracium spp.) * Equisetum (Equisetum spp.) * Maianthemum (Maianthemum bifolium) * Juniperus (Juniperus communis) * Neottia cordata (Neottia cordata) * Rubus (Rubus idaeus) * Pleurozium (Pleurozium schreberi) * Hylocomium (Hylocomium splendens) * Crista (Hypnum crista-castrensis) * Scoparium (Dicranum scoparium) * Polysetum (Dicranum polysetum) * Commune (Polytrichum commune) * Juniperinum (Polytrichum juniperinum) * Rhytidiadelphus (Rhytidiadelphus triquetrus) * Sphagnum (Sphagnum spp.) * Ciliare (Ptilidium ciliare) * Plagiochila (Plagiochila asplenioides) * Mnium (Mnium sp.) * Rangiferina (Cladonia rangiferina) * Stellaris (Cladonia stellaris) * Coccifera (Cladonia coccifera) * Uncialis (Cladonia uncialis) * Islandica (Cetraria islandica) * Peltigera (Peltigera leucophlebia) * Stereocaulon (Stereocaulon paschale) * Rock (hits on bare rock) * Deadwood (hits on deadwood) * Slash (hits on harvesting residues) * Biocrust (hits on Biocrust) * Organic (hits on bare organic soil) * Mineral (hits on bare mineral soil) * Ground (Ground hits; used to normalize the number of hits) * Shannon_Index (Shannon Index of understory species) ### **01 Stand Comparison** The “01 Stand Comparison” folder contains tables on soil texture and climate conditions for each stand, which were used to verify site comparability. Soil texture values are given as percentages of fine earth less than or equal to two millimeters, following DIN 18196. Variables include coarse sand (≥0.63 mm), medium sand (≥0.2 mm), fine sand (≥0.063 mm), coarse silt (≥0.02 mm), medium silt (≥0.0063 mm), fine silt (≥0.002 mm), and clay (&lt;0.002 mm). Climate data consist of geographic coordinates, average summer and winter temperatures (in °C), total summer and winter precipitation (in mm/year), and mean daily radiation (in W/m²). These factors were examined through Principal Component Analysis to ensure that stands were comparable across the two chronosequences. The R script "R - Stand comparison" is used for Principal component analysis to check whether abiotic site conditions differed systematically between the chronosequence types. "climate means" contains the following data: * Site (stand name) * Longitude (geogr. longitude in degrees, WGS84) * Latitude (geogr. latitude in degrees, WGS84) * t_mean_avg_Summer (average summer temperature in degrees C) * t_mean_avg_Winter (average winter temperature in degrees C) * precipitation_sum_Summer (average summer precipitation in mm) * precipitation_sum_Winter (average winter precipitation in mm) * mean_daily_radiation (average daily radiation in W/m2) "wide_texture" contains the following data: * Site (this is the name of the stand) * coarse_sand (coarse sand in % of fine earth, for measurement please refer to 01 Stand Comparison) * medium_sand (medium sand in % of fine earth) * fine_sand (fine sand in % of fine earth) * coarse_silt (coarse silt in % of fine earth, for measurement please refer to 01 Stand Comparison) * medium_silt (medium silt in % of fine earth) * fine_silt (fine silt in % of fine earth) * clay (clay in % of fine earth) "Master_Table_2" contains the following data: * Site * Subplot * humus_depth * stoniness * pH * moisture * moisture_class (taken from SLU's soil moisture map; dimensionless) * Management * Time * LAI * Ntot * NO3 * NH4 * Al * B * Ca * Cu * Fe * K * Mg * Mn * Na * P * S * Zn * Myrtillus (Vaccinium myrtillus) * Vitis (Vaccinium vitis-idaea) * Uliginosum (Vaccinium uliginosum) * Empetrum (Empetrum nigrum) * Linnea (Linnaea borealis) * Ledum (Rhododendron tomentosum) * Calluna (Calluna vulgaris) * Huperzia (Huperzia selago) * Annotinum (Lycopodium annotinum) * Complanatum (Lycopodium complanatum) * Lagopus (Plantago lagopus) * Dryopteris (Dryopteris spp.) * Avenella (Avenella flexuosa) * Pilosa (Luzula pilosa) * Globularis (Carex globularis) * Carex sp. (Carex spp.) * Canescens (Carex canescens) * Mellampirum (Melampyrum sylvaticum) * Solidago (Solidago virgaurea) * Pirula (Pyrola secunda) * Epilobium (Chamerion angustifolium) * Rumex (Rumex acetosella) * Trientalis (Trientalis europaea) * Geranium (Geranium sylvaticum) * Chamaemorus (Rubus chamaemorus) * Arcticus (Rubus arcticus) * Saxatilus (Saxifraga spp.) * Hieracium (Hieracium spp.) * Equisetum (Equisetum spp.) * Maianthemum (Maianthemum bifolium) * Juniperus (Juniperus communis) * Neottia cordata (Neottia cordata) * Rubus (Rubus idaeus) * Pleurozium (Pleurozium schreberi) * Hylocomium (Hylocomium splendens) * Crista (Hypnum crista-castrensis) * Scoparium (Dicranum scoparium) * Polysetum (Dicranum polysetum) * Commune (Polytrichum commune) * Juniperinum (Polytrichum juniperinum) * Rhytidiadelphus (Rhytidiadelphus triquetrus) * Sphagnum (Sphagnum spp.) * Ciliare (Ptilidium ciliare) * Plagiochila (Plagiochila asplenioides) * Mnium (Mnium sp.) * Rangiferina (Cladonia rangiferina) * Stellaris (Cladonia stellaris) * Coccifera (Cladonia coccifera) * Uncialis (Cladonia uncialis) * Islandica (Cetraria islandica) * Peltigera (Peltigera leucophlebia) * Stereocaulon (Stereocaulon paschale) * Rock (hits on bare rock) * Deadwood (hits on deadwood) * Slash (hits on harvesting residues) * Biocrust (hits on Biocrust) * Organic (hits on bare organic soil) * Mineral (hits on bare mineral soil) * Ground (Ground hits; used to normalize the number of hits) * total_hits (total number of hits on any plant/lichen) * species_richness (number of species found in the subplot) * vascular_hits (number of hits on vascular plants) * vascular_species (number of vascular plants found in the subplot) * shrubs_hits (number of hits on shrubs) * shrubs_species (number of shrubs found in the subplot) * herbs_hits (number of hits on herbs * herbs_species (number of herbsfound in the subplot) * graminoids_hits (number of hits on graminoids) * graminoids_species (number of graminoids found in the subplot) * saplings_hits (number of hits on saplings) * saplings_species (number of saplings found in the subplot) * bryophtyes_hits (number of hits on bryophtyes) * bryophtyes_species (number of bryophtyes found in the subplot) * lichens_hits (number of hits on lichens) * lichens_species (number of lichens found in the subplot) * Shannon_Index (Shannon Index for the subplot; dimensionless) * Simpson_Index (Simpson Index for the subplot; dimensionless) ### **02 Vegetation Metrics** The 02 Vegetation Metrics folder provides the raw data on understory vegetation composition. Twenty subplots of 0.5 m each were installed in a five-by-four grid in every stand, with each subplot assessed using a point-intersect method adapted from Sundqvist et al. (2011). A metal pin was lowered fifty times in each subplot, and each pin hit was recorded for vascular plants, bryophytes, and lichens. These hits were normalized to 100 for easier comparison among subplots. Species were identified by experts during field surveys from June to September 2023. "Pinpointing Data" contains the following data: * Site (this is the name of the stand) * Subplot * Myrtillus (Vaccinium myrtillus) * Vitis (Vaccinium vitis-idaea) * Uliginosum (Vaccinium uliginosum) * Empetrum (Empetrum nigrum) * Linnea (Linnaea borealis) * Ledum (Rhododendron tomentosum) * Calluna (Calluna vulgaris) * Huperzia (Huperzia selago) * Annotinum (Lycopodium annotinum) * Complanatum (Lycopodium complanatum) * Lagopus (Plantago lagopus) * Dryopteris (Dryopteris spp.) * Avenella (Avenella flexuosa) * Pilosa (Luzula pilosa) * Globularis (Carex globularis) * Carex sp. (Carex spp.) * Canescens (Carex canescens) * Mellampirum (Melampyrum sylvaticum) * Solidago (Solidago virgaurea) * Pirula (Pyrola secunda) * Epilobium (Chamerion angustifolium) * Rumex (Rumex acetosella) * Trientalis (Trientalis europaea) * Geranium (Geranium sylvaticum) * Chamaemorus (Rubus chamaemorus) * Arcticus (Rubus arcticus) * Saxatilus (Saxifraga spp.) * Hieracium (Hieracium spp.) * Equisetum (Equisetum spp.) * Maianthemum (Maianthemum bifolium) * Juniperus (Juniperus communis) * Neottia cordata (Neottia cordata) * Rubus (Rubus idaeus) * Pleurozium (Pleurozium schreberi) * Hylocomium (Hylocomium splendens) * Crista (Hypnum crista-castrensis) * Scoparium (Dicranum scoparium) * Polysetum (Dicranum polysetum) * Commune (Polytrichum commune) * Juniperinum (Polytrichum juniperinum) * Rhytidiadelphus (Rhytidiadelphus triquetrus) * Sphagnum (Sphagnum spp.) * Ciliare (Ptilidium ciliare) * Plagiochila (Plagiochila asplenioides) * Mnium (Mnium sp.) * Rangiferina (Cladonia rangiferina) * Stellaris (Cladonia stellaris) * Coccifera (Cladonia coccifera) * Uncialis (Cladonia uncialis) * Islandica (Cetraria islandica) * Peltigera (Peltigera leucophlebia) * Stereocaulon (Stereocaulon paschale) * Rock (hits on bare rock) * Deadwood (hits on deadwood) * Slash (hits on harvesting residues) * Biocrust (hits on Biocrust) * Organic (hits on bare organic soil) * Mineral (hits on bare mineral soil) * Ground (Ground hits; used to normalize the number of hits) * total_hits (total number of hits on any plant/lichen) ### **03 Aboveground Resources** The “03 Aboveground Resources” folder contains data on leaf area index (LAI) and forest structural complexity, as well as the corresponding .laz files (point clouds) generated by handheld mobile laser scanning (ZEB Horizon, GeoSLAM Ltd). LAI was measured at two meters above ground with a Solariscope (Behling SOL 300), following methods by Schleppi et al. (2007). Forest structural complexity (Db_value) was computed from 3D point clouds using fractal analysis as described by Seidel (2018). The .laz files hold the raw or minimally processed point clouds. These files can be opened with software such as CloudCompare (open-source), LiDAR360, or any LiDAR processing application that supports the .laz format. The folder contains the R script "calculation_box_dimension", which was used to calculate the box dimension (Db_value). "Aboveground Resources" contains the following data: * Site * Subplot * LAI * Db_value ### **04 Belowground Resources** The “04 Belowground Resources” folder contains measurements of soil chemistry and nutrient availability. Ion-exchange resin capsules (UNIBEST Inc.) were inserted at the interface between the organic and mineral soil layers for one growing season (June to September 2023), then extracted with 1M KCl and analyzed via ICP-OES for NH4+, NO3–, Ca2+, and other ions. All ion concentrations are in ppm in extracted solution. Soil pH was measured using a 1:5 soil-to-water ratio in samples taken from the Oa horizon. These procedures were designed to capture belowground resource availability and heterogeneity at each subplot. Belowground Resources" contains the following data: * Site * Subplot * pH * moisture * Ntot * NO3 * NH4 * Al * B * Ca * Cu * Fe * K * Mg * Mn * Na * P * S **05 Statistical Analyses** The “05 Statistical Analyses” folder contains all R scripts used to answer the hypotheses of the study. These scripts outline data loading, cleaning, normalization (e.g., min-max scaling), model building (particularly generalized additive models for alpha and beta diversity), cross-validation, and multivariate ordinations such as NMDS. Each script is annotated to guide users through replicating the analytical steps. "Master_Table" contains the following data: * Site * Subplot * humus_depth * stoniness * pH * moisture * moisture_class * Management * Time * LAI * Ntot * NO3 * NH4 * Al * B * Ca * Cu * Fe * K * Mg * Mn * Na * P * S * Zn * Myrtillus * Vitis * Uliginosum * Empetrum * Linnea * Ledum * Calluna * Huperzia * Annotinum * Complanatum * Lagopus * Dryopteris * Avenella * Pilosa * Globularis * Carex sp. * Canescens * Mellampirum * Solidago * Pirula * Epilobium * Rumex * Trientalis * Geranium * Chamaemorus * Arcticus * Saxatilus * Hieracium * Equisetum * Maianthemum * Juniperus * Neottia cordata * Rubus * Pleurozium * Hylocomium * Crista * Scoparium * Polysetum * Commune * Juniperinum * Rhytidiadelphus * Sphagnum * Ciliare * Plagiochila * Mnium * Rangiferina * Stellaris * Coccifera * Uncialis * Islandica * Peltigera * Stereocaulon * Rock * Deadwood * Slash * Biocrust * Organic * Mineral * Ground * total_hits * species_richness * vascular_hits * vascular_species * shrubs_hits * shrubs_species * herbs_species * graminoids_hits * graminoids_species * saplings_hits * saplings_species * bryophtyes_hits * bryophtyes_species * lichens_hits * lichens_species * Shannon_Index * Simpson_Index * Db_value "plot_level_data" contains the following data: * Site * Time * Management * LAI * LAI_normalized * LAI_cv_normalized * Db_value * Db_value_cv * pH * moisture Soil nutrients * Ntot * NO3 * NH4 * Al * B * Ca * Cu * Fe * K * Mg * Mn * Na * P * S * Zn * pH_cv * moisture_cv * LAI_cv * Ntot_cv * NO3_cv * NH4_cv * Al_cv * B_cv * Ca_cv * Cu_cv * Fe_cv * K_cv * Mg_cv * Mn_cv * Na_cv * P_cv * S_cv * Zn_cv * AverageDissimilarity * ARA * ARH * BRA * BRH ### **06 Point clouds** The 06 Point clouds folder contains the compressed point clouds from the laser scanning. They would belong in the "03 Aboveground resources" folder, yet, to avoid the download of these large files, they were outsourced to this separate folder. ## SOFTWARE REQUIREMENTS All data analyses were conducted using R (version 4.0.0 or higher) and RStudio (version 2023.03.0, Build 386). The principal R packages include tidyverse for data wrangling, vegan for ecological analysis and ordinations, mgcv for fitting generalized additive models, MuMIn for model selection (dredge), gratia for diagnostics and plotting of GAMs, and caret for cross-validation. The .laz point clouds in folder 03 Aboveground Resources can be opened with CloudCompare or similar LiDAR processing software. No additional tools are strictly required beyond these applications and standard PDF or CSV readers. DATA COLLECTION AND SAMPLING DESIGN Field sampling took place from June to September 2023 in 36 Scots pine stands across Northern Sweden. Eighteen stands underwent clear-cutting between 1 and 109 years ago, forming the Rotational Management Chronosequence, while eighteen stands experienced wildfire between 4 and 375 years ago, forming the Unmanaged Fire Chronosequence. In each stand, twenty 0.5 m subplots were established in a grid, providing a consistent spatial arrangement for measuring vegetation composition, LAI, and soil properties. Standard quality checks were performed throughout data collection, including validation of species identifications, inspection of outliers, and calibration of instruments. ## CONTACT AND SUPPORT For questions regarding the data, methodology, or potential collaborations, please contact: [vincent.buness@slu.se](mailto:vincent.buness@slu.se) [michael.gundale@slu.se](mailto:michael.gundale@slu.se) This README is intended to guide users in reproducing or extending the analyses detailed in the manuscript, covering all aspects of data collection, processing, and modeling. The combination of R scripts, CSV files, .laz point clouds, and explanatory documentation should allow researchers to investigate how disturbances shape resource availability and heterogeneity, and consequently influence understory diversity in boreal forest ecosystems. References: Schleppi, P., Conedera, M., Sedivy, I. and Thimonier, A. 2007. Correcting non-linearity and slope effects in the estimation of the leaf area index of forests from hemispherical photographs. Agric. For. Meteorol. 144: 236242. [https://doi.org/10.1016/j.agrformet.2007.02.004](https://doi.org/10.1016/j.agrformet.2007.02.004) Seidel, D. 2018. A holistic approach to determine tree structural complexity based on laser scanning data and fractal analysis. Ecol. Evol. 8: 128134. [https://doi.org/10.1002/ece3.3661](https://doi.org/10.1002/ece3.3661) Sundqvist, M. K., Giesler, R., Graae, B. J., Wallander, H., Fogelberg, E. and Wardle, D. A. 2011. Interactive effects of vegetation type and elevation on aboveground and belowground properties in a subarctic tundra. Oikos 120: 128142. [https://doi.org/10.1111/j.1600-0706.2010.18811.x](https://doi.org/10.1111/j.1600-0706.2010.18811.x)