DOI: 10.5061/dryad.j9kd51cj2
title: Use climatic space-for-time substitutions with care: not only climate, but also local environment affect performance of the key forest species bilberry along elevation gradient

description: An urgent aim of ecology is to understand how key species relate to
 climatic and environmental variation, to better predict their prospects
 under future climate change. The abundant dwarf shrub bilberry (Vaccinium
 myrtillus L.) has caught particular interest due to its uphill expansion
 into alpine areas. Species’ performance under changing climate has been
 widely studied using the climatic space-for-time approach along elevation
 gradients, but potentially confounding, local environmental variables that
 vary along elevation gradients have rarely been considered. In this study,
 performed in ten sites along an elevation gradient (200–875 m) in W
 Norway, we recorded species composition and bilberry performance, both
 vegetative (ramet size and cover) and reproductive (berry and seed
 production) properties, over one to four years. We disentangled effects of
 local environmental variables and between-year, climatic variation
 (precipitation and temperature), and identified shared and unique
 contributions of these variables by variation partitioning. We found
 bilberry ramet size, cover, and berry production to peak at intermediate
 elevations, whereas seed production increased upwards. The peaks were less
 pronounced in extreme (dry or cold) summers than in normal summers. Local
 environmental variables explained much variation in ramet size and cover,
 less in berry production, and showed no relation to seed production.
 Climatic variables explained more of the variation in berry and seed
 production than in ramet size and cover, with temperature relating to
 vegetative performance, and precipitation to reproductive performance.
 Bilberry’s clonal growth and effective reproduction probably explain why
 the species persists in the forest and at the same time invades alpine
 areas. Our findings raise concerns about the appropriateness of the
 climatic space-for-time approach. We recommend including both climatic and
 local environmental variables in studies of variation along elevation
 gradients, and conclude that variation partitioning can be a useful
 supplement to other methods for analysing variation in plant performance.
description: We studied bilberry
 performance in 2017–2020 along an elevation gradient in a boreal,
 bilberry-dominated Scots pine (<em>Pinus sylvestris</em>)
 forest in Sogndal, Vestland county, W Norway (61°13’N, 7°9’E). The study
 site extended along a 2.7 km SW-facing ridge, covering the altitudinal
 interval 200–875 m a.s.l., from dense lowland forest up to the alpine tree
 line. In 2017, we subjectively placed ten blocks of 5
 × 10 m along the elevation gradient, all facing S–SW with an average slope
 of 10–30 °. Each block spanned the variation from open to shaded sites. In
 each block, we randomly placed and permanently marked five plots of 0.5 ×
 0.5 m with a minimum between-plot distance of 1 m.
 We collected six data sets in
 the 50 plots; (1) species composition in 2017, (2) bilberry vegetative
 performance (ramet size) in 2017-2018, (3) bilberry vegetative performance
 (bilberry cover) in 2017, (4) bilberry reproductive performance (berry
 production) in 2017-2020, (5) bilberry reproductive performance (seed
 production) in 2017-2018, and (6) environmental data in
 2017.  1. <em><strong>Species
 composition</strong></em>. We divided each plot into 16 subplots, each
 0.0156 m<sup>2</sup>. In June–July 2017, we recorded the presence or
 absence of vascular plants, bryophytes, and lichens in each subplot and
 calculated subplot frequency as a measure of species'
 abundance. 2. <em><strong>Bilberry vegetative
 performance - ramet size.</strong></em> We tagged selected bilberry ramets in each plot
 by applying a split, coloured Hama maxi bead to the main twig of each
 ramet (Hegland et al., 2010). Ramet selection, performed in 2017, started
 in one of the four central subplots and continued in an anticlockwise
 manner by including additional subplots until eight ramets were included.
 The total number of ramet tagged in 2017 was 272, of which 15 could not be
 re-located at the 2018 census. That year we included all fertile ramets in
 each plot, thereby increasing the total number of tagged ramets by 108. A
 total of 46 and 40 plots contained berry-producing ramets in 2017 and
 2018, respectively. In
 2017 and 2018, we measured plant height (H), stem diameter (DS), and
 number of annual shoots (AS) for all tagged ramets and calculated dry mass
 (DM) of each ramet as a non-destructive estimate of plant size, using the
 formula described by Hegland et al. (2010):
 log<sub>2</sub>(DM) = 1.41700 × log<sub>2</sub>
 (DS) + 0.97104 × log<sub>2</sub> (H) + 0.44153 ×
 log<sub>2</sub> (AS + 1) –7.52070. The formula is based upon a
 model developed from a representative, destructively sampled data set
 (R<sup>2</sup> = 0.944, n = 150).
 3. <em><strong>Bilberry vegetative
 performance - bilberry cover</strong></em>. We visually
 estimated percent cover of bilberry in each plot.
 4.
 <em><strong>Bilberry reproductive performance - berry
 production</strong></em>. Each year from 2017 to 2020, we counted the
 total number of berries produced in each 0.25 m<sup>2</sup>
 plot and multiplied this number by four to express berry production as
 berries per square meter (m<sup>-2</sup>).
 5.
 <em><strong>Bilberry reproductive performance - seed
 production</strong></em>. In 2017 and 2018, we also counted seeds. We
 collected all berries produced by the tagged ramets in each plot, dried
 them in a drying cabinet
 at 70 °C for 36 hours, and weighed them individually. For each plot the
 berry closest to plotwise mean mass was selected, dissected under a stereo
 microscope, and the number of mature (&gt; 1 mm long, filled) and
 non-mature seeds (aborted seeds or unfertilised ovules; &lt; 1 mm,
 flat or negligibly swollen) were counted. The counts were used to
 calculate two measures of seed production per plot: the number of mature
 seeds, and the fraction of mature to total (mature and immature) seeds,
 per berry (Pato and Obeso, 2012a).
 6.
 <em><strong>Environmental data</strong></em>.
 A set of 16 environmental
 variables that could potentially influence species composition and
 bilberry performance, was recorded for each of the 50 plots based on field
 measurements or soil samples taken in 2017. We recorded soil chemical and
 physical variables for the 50 plots from soil samples obtained by mixing
 four soil sub-samples taken from the upper 10 cm of the humus layer in
 each plot. Soil samples were dried at 35 °C for ten days in drying
 cabinets before sifting (2 mm mesh width). pH and soil organic matter
 (SOM) were measured at the soil laboratory at Western Norway University of
 Applied Sciences, Sogndal, by procedures described by Krogstad (1992). All
 other soil analyses were carried out at the soil laboratory of the
 Norwegian University of Life Sciences, Ås. Total N was determined by the
 Dumas method. Other soil elements were extracted in a
 NH<sub>4</sub>NO<sub>3</sub> solution (Stuanes et
 al., 1984) for determination of exchangeable element concentrations by
 ICP. Among these, we used elements regarded as macronutrients,
 micronutrients, or toxic to plants (P, K, Al, Ca, Fe, K, Mg, Mn, S and Zn)
 for further analyses. We recalculated all elements as ppm (parts per
 million) by multiplication with 1/SOM (Økland, 1988). We recorded soil
 moisture in August 2017 after four days without rainfall, using an AT
 Delta-T moisture meter, type HH2 SM300 v 4.0. The mean moisture, used in
 analyses, was calculated from four measurements per plot. Soil depth was
 measured as the distance a steel rod (diameter 1 cm) could be driven into
 the soil at eight fixed positions 10 cm outside the plot border. The
 median of these eight values was used in the analyses. The Heat index (HI)
 was calculated according to Heikkinen (1991) by measuring aspect and slope
 (0–360° scale) using a clinometer compass held at a position considered
 representative for each plot. We measured canopy openness by use of a
 convex, spherical densiometer with 24 squares (Lemmon, 1956). Canopy
 openness (Light) was estimated as the number of squares not containing
 canopy.
description: All data are stored in an
 Excel Workbook, in separate sheets.
description: ## Description of the data and file structure We studied bilberry
 performance in 20172020 along an elevation gradient in a boreal,
 bilberry-dominated Scots pine (Pinus sylvestris) forest. The study site
 extended along a 2.7 km SW-facing ridge, covering the altitudinal interval
 200875 m a.s.l., from dense lowland forest up to the alpine tree line. In
 2017, we subjectively placed ten blocks of 5 10 m along the elevation
 gradient, all facing SSW with an average slope of 1030 . Each block
 spanned the variation from open to shaded sites. In each block, we
 randomly placed and permanently marked five plots of 0.5 0.5 m with a
 minimum between-plot distance of 1 m. We collected six data setsin the 50
 plots; (1) species composition in 2017 (2) bilberry vegetative performance
 (ramet size) in 2017-2018 (3) bilberry vegetative performance (bilberry
 cover) in 2017 (4) bilberry reproductive performance; berry production in
 2017-2020 (5) bilberry reproductive performance; seed production in
 2017-2018 (6) environmental data in 2017. Dataset 1 contains records of 63
 species in the 50 plots, recorded in 2017 on a 0-16 range. Dataset 2
 contains 635 estimates of the dry weight of 353 unique ramets over two
 years (20172018). Dataset 3 contains records of percentage cover of
 bilberry in each of the fifty plots in 2017. Dataset 4 contains records of
 berry density (number of berries per square meter) in the 50 plots over
 four years (20172020). Dataset 5 contains records of mature and aborted
 seeds in the median berry per plot, over two years (2017-2018) Dataset 6
 contains data on environmental conditions in each of the 50 plots. We
 recalculated all elements as ppm (parts per million) by multiplication
 with 1/SOM. We recorded soil moisture in August 2017 after four days
 without rainfall. The mean moisture, used in analyses, was calculated from
 four measurements per plot. Soil depth was measured as the distance a
 steel rod (diameter 1 cm) could be driven into the soil at eight fixed
 positions 10 cm outside the plot border. The median of these eight values
 was used in the analyses. The Heat index (HI) was calculated according to
 Heikkinen (1991) by measuring aspect and slope (0360 scale) using a
 clinometer compass held at a position considered representative for each
 plot. We measured canopy openness by use of a convex, spherical
 densiometer with 24 squares (Lemmon, 1956). Canopy openness (Light) was
 estimated as the number of squares not containing canopy. The first tab of
 the Excel file has the variable listing and their definitions.