DOI: 10.5061/dryad.g4f4qrfz5
title: Data from: Rate of permafrost thaw and associated plant community dynamics in peatlands of northwestern Canada

description: This dataset was collected to document the changing plant community, and
 associated environmental factors, as warming climate conditions accelerate
 permafrost thaw in northern peatland environments. Due to the insulative
 properties of dry, surface peat layers, discontinuous permafrost is
 preferentially found in peatlands, termed peat plateaux, where the
 volumetric expansion of ice-rich permafrost has resulted in a raised, dry
 ground surface dominated by lichens and, often, stunted black spruce
 forests. As ground temperatures warm, and the ice-rich permafrost thaws,
 the ground surface sinks to, or below, the water table, and these peat
 plateau environments change dramatically from black spruce and
 lichen-dominated peat plateaux to treeless moss- and sedge-dominated
 collapse scar environments. Data are from a set of 17 sites distributed
 along a latitudinal gradient in the Mackenzie Valley of Northwestern
 Canada. At each site, a transect of five to nine contiguous 1x1m quadrats
 was sampled, spanning the transition from peat plateau to collapse scar
 environments and, thus, capturing the zone of active permafrost thaw
 within peat plateaux as they transition to collapse scars. Fourteen of
 these sites were sampled at two time periods: 2007 and 2008 (T1: time 1),
 and 2017 and 2018 (T2: time 2) enabling an assessment of 10-year changes
 (9 years for one site). This dataset includes quadrat-level
 measurements of plant community composition (percent cover by species),
 frost depth, water table depth, peat depth, soil moisture, and canopy
 cover. Site level measurements consist of maximum peat depth, along with
 pH and electrical conductivity of collapse scar water samples, as well as
 the annual rate of lateral permafrost thaw. We also include basic site
 location parameters, as well as several climatic parameters, interpolated
 for each site using BioSIM software.
description: <em>Please note that these methods have been adapted from
 those in the accompanying paper. Please see the accompanying paper in the
 Journal of Ecology for full context.  </em>
 Field Sampling In the
 summers (late June to early September) of 2007 and 2008 (T1: time 1), 16
 sites were selected along a latitudinal gradient in the Mackenzie Valley
 and a single transect was established at each site, perpendicular to the
 boundary of a collapse scar feature. Each transect was located on an
 independent peat plateau and consisted of a series of five to nine
 contiguous 1- x 1- m vegetation quadrats extending from the peat plateau
 to the collapse scar proper. The transect length (and, thus, the number of
 quadrats) depended on the distance needed to span this transition such
 that the transect extended from one or two quadrats into the peat plateau
 beyond the ‘collapsing slope’ to a point in the collapse scar where
 vegetation became more consistent and typical of that found in central
 portions of the collapse scar. Fourteen of these sites were resampled in
 the summers of 2017 and 2018 (T2: time 2) enabling an assessment of
 10-year changes (9 years for one site). At one site the transect was
 resampled, although it could not be precisely relocated; a second was
 partially consumed by a wildfire in 2014 so was not resampled. Two new
 transects were established in T2. A metal rod (peat probe) was used to
 characterize the depth to permafrost at least every 100 cm along the
 transect at both time periods, with the lateral extent of permafrost
 determined to the nearest 10 cm. During the first measurement (T1), the
 peat probe was also used to assess peat depth at transect locations where
 permafrost was not present. Also, at T1, composite surface water samples
 were collected from three shallow pits dug in the central portions of each
 collapse scar. Water samples were frozen for transportation to the
 laboratory, subsequently thawed, and analyzed for pH and electrical
 conductivity using a Mettler Toledo multi-parameter meter (Mettler Toledo,
 Mississauga, Canada) with conductivity measurements standardized to 25°C.
 At T2, depth to the water table was assessed to the nearest 5 cm every 100
 cm along each transect using a syringe to pull water from a piece of 0.5
 cm diameter tubing marked at 5cm intervals. Also at T2, a Fieldscout TDR
 100 soil moisture metre (Spectrum Technologies, Inc., Aurora, USA) was
 used to measure volumetric soil water content to a 12 cm depth as an
 average of three readings, from the approximate midpoint of three sides of
 each vegetation quadrat. Canopy closure was assessed in both time periods
 with the use of a concave spherical densitometer held at 1.3 m above each
 vegetation quadrat. Also at both time periods, within
 each quadrat, all vascular plants, bryophytes, and ground lichens were
 identified to species and cover was visually estimated to the nearest
 percent with cover values of 0.5% and + (&lt; 0.5% cover) also
 recorded. For quantitative analyses, + was interpreted as a cover value of
 0.05%. Species concepts and nomenclature largely follow those outlined in
 the Flora of North America for mosses (Flora of North America Editorial
 Committee, 2007; 2014), and vascular plants (Flora of North America
 Editorial Committee, 1993+). For families for which Flora of North America
 volumes have not yet been completed, vascular species concepts follow
 Porsild and Cody (1980) and Cody (2000), all updated according to VASCAN
 for the most current nomenclature (Brouillet et al., 2010+).  Liverwort
 species concepts generally follow Schuster (1966-1992) and Paton (1999),
 while those of lichens follow Brodo et al. (2001), Goward (1999), and
 Goward et al. (1994), updated to the most current taxonomy according to
 Stotler and Crandall-Stotler (2017) and Esslinger (2021), respectively. 
 One exception is the lichen genus of <em>Cladina</em> (the
 classic “reindeer lichen” species) which current taxonomy places within
 <em>Cladonia</em> (Esslinger, 2021; Ahti and DePriest, 2001). 
 Due to clear functional and morphological distinctions between the species
 groups, we maintain the older distinction of <em>Cladina</em>
 as a separate genus (Ahti 1984).  Data
 processing <em>Environmental
 variables</em> Because of the challenge of
 measuring relevant abiotic variables across the disparate conditions
 present along the gradient from peat plateau to collapse scar
 environments, several environmental variables were modified to allow for
 more complete datasets. In particular, the depth to the water table was
 adjusted (WTD<sub>adj</sub>) to account for situations when
 the water table was not detected via the siphon system. In all but one
 case these missing values were in locations directly overlying permafrost
 and the WTD<sub>adj</sub> was set to the frost depth where the
 water table was presumably present but frozen. At one other location, on a
 relatively dry hummock where the water table was deeper than the 45 cm
 siphon assembly, the water table was assigned an estimated value of 50 cm
 to complete the dataset. In cases where permafrost was not present in the
 collapse scar, frost depth (FD<sub>adj</sub>) was standardized
 to 200 cm, well beyond the deepest permafrost detected in these transects
 (139 cm). We recognize that permafrost is likely absent from many of these
 locations but we cannot rule out its presence at depths below the
 peat/mineral interface (165 – 500 cm). Fundamentally,
 FD<sub>adj</sub> is a pseudo-quantitative variable with values
 of 200 cm indicating an absence of near-surface permafrost.
 Peat depth was measured only at T1 and could not be measured in
 quadrats with permafrost. Since the permafrost table restricts the rooting
 zone and any hydrochemical interactions with the underlying mineral soil,
 peat depths were considered to have limited ecological relevance for
 quadrats with permafrost; thus, quadrats having permafrost present in both
 T1 and T2 were assigned a value of zero for peat depth. When peat depth
 measurements were available for a quadrat in T1, that value was also used
 for T2. For those quadrats where permafrost thawed between T1 and T2 and,
 thus, lacked measurements from T1, peat depths were assigned for T2 based
 on the mean peat depth of other quadrats in the transect. We feel this is
 justified as peat depth is a relatively coarsely measured value with
 limited small-scale variability and, within a transect, peat depth values
 displayed a mean range of 50 ± 12 cm (SE). Maximum peat depth
 (PD<sub>max</sub>) for each transect was also used as a
 site-level variable. Probe depth (probed) was the maximum depth the probe
 reached before reaching either frost or mineral soil and is, thus,
 effectively, a composite of the unadjusted frost depth and the peat
 depth. The variable “distance from collapse” (dfc)
 characterized quadrats in terms of their position relative to the location
 of permafrost collapse. The point along each quadrat where peat plateau
 permafrost was last detected was set to a distance of dfc = 0 and the
 centre point of each quadrat from this “point of collapse” was calculated
 such that distances farther into the collapse scar was positive and
 distances farther into the peat plateau were negative. The mean annual
 lateral rate of permafrost thaw for each transect was calculated as the
 difference between the distance of maximum permafrost extent at T1 versus
 T2, divided by the sampling interval (9 or 10 years).
 <em>Diversity metrics</em>
 Shannon diversity (H’), Simpson’s diversity index (1-D), Pielou’s
 evenness (J), and species richness (S) were calculated for each quadrat
 using the <em>vegan</em> package (Oksanen et al., 2022) of R,
 version 4.2.1 (R Core Team, 2022). Species richness and abundance (%
 cover) were also calculated for several structural groups and key genera
 (vascular species, bryophytes, mosses, liverworts, lichens, trees, shrubs,
 forbs, graminoids, <em>Sphagnum</em>,
 <em>Carex</em>, <em>Eriophorum</em>,
 <em>Cladina</em>). <em>Climate
 variables</em> Since climate stations in
 northern Canada are very scarce, we used climate data interpolated from
 the nearest measurement locations using BioSIM software (Régnière et al.,
 2014). The algorithm used inverse distance square-weighted interpolation
 of data from the eight closest weather stations to each study site,
 correcting for differences in latitude and elevation between the weather
 stations and the study sites. Four climatic variables were used: the mean
 annual temperature (MAT), the mean annual precipitation (MAP), mean annual
 snowfall (MAS) and the mean length of the growing season (GS); each of
 these was calculated for three time periods: 1950-2018 (the maximum length
 of time we have reasonably consistent climate records in the major NWT
 climate stations); 1978-2007 (the 30-years preceding T1); and 1988-2017
 (the 30 years preceding T2). The 30-year time frame was used to avoid
 short-term trends and reflect longer-term climate conditions, as is the
 standard reporting procedure for climate normals, recommended by the World
 Meteorological Organization (WMO 2022). Changes in climate conditions
 (ΔMAT, ΔMAP, ΔMAS, and ΔGS) were quantified through subtraction of
 1978-2007 means from those of 1988-2017. Growing season was defined as the
 number of days between the last 3 consecutive days with frost (minimum
 daily temperature (T<sub>min</sub>) &lt; 0) in the spring
 and the first 3 consecutive days with frost (T<sub>min</sub>
 &lt; 0) in the autumn. Mean seasonal values, and 10-year changes, of
 temperature, precipitation, and snowfall were also calculated over the
 same time periods where seasons were considered to be spring (March,
 April, May), summer (June, July, August), autumn (September, October,
 November), and winter (December of the previous year, January,
 February). Literature
 Cited Ahti, T. 1984. The status
 of <em>Cladina</em> as a genus segregated from
 <em>Cladonia</em>. Nova Hedwigia
 <strong>79</strong>: 25-61.   Ahti, T.
 and P.T. DePriest.  2001.  New combinations of
 <em>Cladina</em> epithets in <em>Cladonia</em>
 (Ascomycotina: Cladoniaceae).  Mycotaxon <strong>78</strong>:
 499-502. Brodo, I.M., S.D. Sharnoff, and S.
 Sharnoff.  2001.  Lichens of North America.  Yale University Press, New
 Haven and London.  795 pp.  Brouillet, L., F.
 Coursol, S.J. Meades, M. Favreau, M. Anions, P. Bélisle & P.
 Desmet. 2010+. VASCAN, the Database of Vascular Plants of Canada.
 http://data.canadensys.net/vascan/ (consulted on 2021-06-24)
 Cody, W.J.  2000.  Flora of the Yukon Territory. 
 2<sup>nd</sup> Edition.  NRC Research Press, Ottawa.  669
 pp.  Esslinger, T.L. 2021. A cumulative checklist for
 the lichen-forming, lichenicolous and allied fungi of the Continental
 United States and Canada, version 24. Opuscula Philolichenum
 <strong>20</strong>: 100-394. Flora of
 North America Editorial Committee, eds. 1993+. Flora of North America
 North of Mexico. 22+ vols. Oxford University Press.  New York and
 Oxford. Flora of North America Editorial Committee,
 eds.  2007.  Flora of North America North of Mexico: Bryophytes: Mosses,
 part 1.  Volume 27.  Oxford University Press.  New York and Oxford.  734
 pp. Flora of North America Editorial Committee, eds. 
 2014.  Flora of North America North of Mexico: Bryophytes: Mosses, part
 2.  Volume 28.  Oxford University Press.  New York and Oxford.  736
 pp. Goward, T.  1999.  The lichens of British
 Columbia Illustrated Keys Part 2 – Fruticose Species.  Special Report
 Series 9, Ministry of Forests Research Program.  Crown Publications,
 Victoria.  327 pp. Goward, T, B. McCune, and D.V.
 Meidinger.  1994.  The lichens of British Columbia Illustrated Keys Part 1
 – Foliose and Squamulose Species.  Special Report Series 8, Ministry of
 Forests Research Program.  Crown Publications, Victoria.  181
 pp. Oksanen, J., G.L. Simpson, F.G. Blanchet, R.
 Kindt, P. Legendre, P.R. Minchin, R.B. O'Hara, P. Solymos, M.H.H.
 Stevens, E. Szoecs, H. Wagner, M. Barbour, M. Bedward, B. Bolker, D.
 Borcard, G. Carvalho, M. Chirico, M. De Caceres, S. Durand, H.B.A.
 Evangelista, R. FitzJohn, M. Friendly, B. Furneaux, G. Hannigan, M.O.
 Hill, L. Lahti, D. McGlinn, M.-H. Ouellette, E.R. Cunha, T. Smith, A.
 Stier, C.J.F. Ter Braak and J. Weedon. 2022. vegan: Community Ecology
 Package. R package version 2.6-4.
 https://CRAN.R-project.org/package=vegan Paton, J.A. 
 1999.  The liverwort flora of the British Isles.  Harley Books,
 Colchester, UK.  626 pp.  Porsild, A.E. and Cody,
 W.J.  1980.  Vascular Plants of Continental Northwest Territories, Canada.
 National Museum of National Sciences, Ottawa.  667 pp. 
 R Core Team. 2022. R: A language and environment for statistical
 computing. R Foundation for Statistical Computing, Vienna, Austria. URL
 https://www.R-project.org/. Régnière, J., R.
 Saint-Amant, and A. Béchard. 2014. BioSIM 10 – User’s manual. Natural
 Resources Canada, Canadian Forest Service, Laurentian Forestry Centre,
 Québec (Quebec). Information Report LAU-X-137E
 Schuster, R. M. 1966-1992. The Hepaticae and Anthocerotae of
 North America East of the Hundredth Meridian. 6 vols. New York, London and
 Chicago. Stotler, R. E. and B. Crandall-Stotler.
 2017. A synopsis of the liverwort flora of North America north of Mexico.
 Annals of the Missouri Botanical Garden
 <strong>102</strong>:574-709. World
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description: # Rate of permafrost thaw and associated plant community dynamics in
 peatlands of northwestern Canada
 [https://doi.org/10.5061/dryad.g4f4qrfz5](https://doi.org/10.5061/dryad.g4f4qrfz5) ## Description of the data and file structure Detailed data collection and processing methods can be found in the associated paper (Errington et al. in press) and in the Dryad metadata. Further contextual information regarding the broader Mackenzie Valley Permanent Monitoring Plot Network, including site-level and forest mensuration plot data can be found in Errington et al. (2018, 2020).  This submission contains 4 .csv files: * “env_transect.csv” contains site/ transect level environmental variables, * “env_quadrat.csv” contains quadrat-level environmental and vegetation summary variables, * “community.csv” contains quadrat-level plant community composition, and * “species.csv” contains species information.  Missing data is coded as “na”.  Field names and contents are clarified below: ### **Table 1: Explanation and description of fields included in “env\_transect.csv”** | **Field** | **Description** | | :--------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | site | site name, consistent with the naming scheme of the Mackenzie Valley Permanent Monitoring Plot Network (Errington et al. 2018, 2020) | | transect | within site transect identifier | | pfzone | Permafrost Zone (Heginbottom et al. 1995) where C = Continuous; ED = Extensive Discontinuous; SD = Sporadic Discontinuous | | ecoclim | Ecoclimatic Region (Ecosystem Classification Group 2007, 2010) where MB = Mid-boreal; HB = High Boreal; LS = Low Subarctic | | lat | latitude (decimal degrees) | | long | longitude (decimal degrees) | | yr-est | year transect established | | yr-rem | year transect remeasured | | expansion | distance (cm) between the lateral extent of permafrost from the establishment year to the remeasurement year; positive values indicate a regression of permafrost into the peat plateau while positive values indicate expansion of permafrost into the collapse scar. | | annualexp | mean annual movement of the lateral permafrost extent (cm yr-1); positive values indicate a regression of permafrost into the peat plateau while positive values indicate expansion of permafrost into the collapse scar. | | compare | y = suitable for remeasurement comparison; n = not suitable for remeasurement comparison; fire = burned in 2014 forest fire | | pH | pH of collapse scar surface water | | cond | electrical conductivity of collapse scar surface water (μS) | | pdmax | maximum peat depth as measured along the transect (cm) | | MAT | mean annual temperature (deg C) 1950-2018 | | MAP | mean annual precipitation (mm) 1950-2018 | | MAS | mean annual snowfall (mm water equivalent) 1950-2018 | | GS | growing season length (days) 1950-2018 | | MAT-T1 | mean annual temperature (deg C) 1978-2007 | | MAP-T1 | mean annual precipitation (mm) 1978-2007 | | MAS-T1 | mean annual snowfall (mm water equivalent) 1978-2007 | | GS-T1 | growing season length (days) 1978-2007 | | MAT-T2 | mean annual temperature (deg C) 1988-2017 | | MAP-T2 | mean annual precipitation (mm) 1988-2017 | | MAS-T2 | mean annual snowfall (mm water equivalent) 1988-2017 | | GS-T2 | growing season length (days) 1988-2017 | | MATdif | change in mean annual temperature (deg C) T2 (1988-2017) - T1 (1978-2007) | | MAPdif | change in mean annual precipitation (mm) T2 (1988-2017) - T1 (1978-2007) | | MASdif | change in mean annual snowfall (mm water equivalent) T2 (1988-2017) - T1 (1978-2007) | | GSdif | change in growing season length (days) T2 (1988-2017) - T1 (1978-2007) | | MATsp | mean annual spring (March, April, May) temperature (deg C) 1950-2018 | | MAPsp | mean annual spring (March, April, May) precipitation (mm) 1950-2018 | | MASsp | mean annual spring (March, April, May) snowfall (mm water equivalent) 1950-2018 | | MATsum | mean annual summer (June, July, August) temperature (deg C) 1950-2018 | | MAPsum | mean annual summer (June, July, August) precipitation (mm) 1950-2018 | | MASsum | mean annual summer (June, July, August) snowfall (mm water equivalent) 1950-2018 | | MATf | mean annual autumn (September, October, November) temperature (deg C) 1950-2018 | | MAPf | mean annual autumn (September, October, November) precipitation (mm) 1950-2018 | | MASf | mean annual autumn (September, October, November) snowfall (mm water equivalent) 1950-2018 | | MATw | mean annual winter (December, January, February) temperature (deg C) 1950-2018 | | MAPw | mean annual winter (December, January, February) precipitation (mm) 1950-2018 | | MASw | mean annual winter (December, January, February) snowfall (mm water equivalent) 1950-2018 | | MATw1 | mean annual winter (December, January, February) temperature (deg C) 1978-2007 | | MAPw1 | mean annual winter (December, January, February) precipitation (mm) 1978-2007 | | MASw1 | mean annual winter (December, January, February) snowfall (mm water equivalent) 1978-2007 | | MATsp1 | mean annual spring (March, April, May) temperature (deg C) 1978-2007 | | MAPsp1 | mean annual spring (March, April, May) precipitation (mm) 1978-2007 | | MASsp1 | mean annual spring (March, April, May) snowfall (mm water equivalent) 1978-2007 | | MATsum1 | mean annual summer (June, July, August) temperature (deg C) 1978-2007 | | MAPsum1 | mean annual summer (June, July, August) precipitation (mm) 1978-2007 | | MASsum1 | mean annual summer (June, July, August) snowfall (mm water equivalent) 1978-2007 | | MATf1 | mean annual autumn (September, October, November) temperature (deg C) 1978-2007 | | MAPf1 | mean annual autumn (September, October, November) precipitation (mm) 1978-2007 | | MASf1 | mean annual autumn (September, October, November) snowfall (mm water equivalent) 1978-2007 | | MATw2 | mean annual winter (December, January, February) temperature (deg C) 1988-2017 | | MAPw2 | mean annual winter (December, January, February) precipitation (mm) 1988-2017 | | MASw2 | mean annual winter (December, January, February) snowfall (mm water equivalent) 1988-2017 | | MATsp2 | mean annual spring (March, April, May) temperature (deg C) 1988-2017 | | MAPsp2 | mean annual spring (March, April, May) precipitation (mm) 1988-2017 | | MASsp2 | mean annual spring (March, April, May) snowfall (mm water equivalent) 1988-2017 | | MATsum2 | mean annual summer (June, July, August) temperature (deg C) 1988-2017 | | MAPsum2 | mean annual summer (June, July, August) precipitation (mm) 1988-2017 | | MASsum2 | mean annual summer (June, July, August) snowfall (mm water equivalent) 1988-2017 | | MATf2 | mean annual autumn (September, October, November) temperature (deg C) 1988-2017 | | MAPf2 | mean annual autumn (September, October, November) precipitation (mm) 1988-2017 | | MASf2 | mean annual autumn (September, October, November) snowfall (mm water equivalent) 1988-2017 | | MATwdiff | change in mean annual winter (December, January, February) temperature (deg C) T2 (1988-2017) - T1 (1978-2007) | | MAPwdiff | change in mean annual winter (December, January, February) precipitation (mm) T2 (1988-2017) - T1 (1978-2007) | | MASwdiff | change in mean annual winter (December, January, February) snowfall (mm water equivalent) T2 (1988-2017) - T1 (1978-2007) | | MATspdiff | change in mean annual spring (March, April, May) temperature (deg C) T2 (1988-2017) - T1 (1978-2007) | | MAPspdiff | change in mean annual spring (March, April, May) precipitation (mm) T2 (1988-2017) - T1 (1978-2007) | | MASspdiff | change in mean annual spring (March, April, May) snowfall (mm water equivalent) T2 (1988-2017) - T1 (1978-2007) | | MATsumdiff | change in mean annual summer (June, July, August) temperature (deg C) T2 (1988-2017) - T1 (1978-2007) | | MAPsumdiff | change in mean annual summer (June, July, August) precipitation (mm) T2 (1988-2017) - T1 (1978-2007) | | MASsumdiff | change in mean annual summer (June, July, August) snowfall (mm water equivalent) T2 (1988-2017) - T1 (1978-2007) | | MATfdiff | change in mean annual autumn (September, October, November) temperature (deg C) T2 (1988-2017) - T1 (1978-2007) | | MAPfdiff | change in mean annual autumn (September, October, November) precipitation (mm) T2 (1988-2017) - T1 (1978-2007) | | MASfdiff | change in mean annual autumn (September, October, November) snowfall (mm water equivalent) T2 (1988-2017) - T1 (1978-2007) | ### **Table 2: Explanation and description of fields included in “env\_quadrat.csv”** | **Field** | **Description** | | :--------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | quadratID | unique quadrat identifier in the form "time period code.site code.quadrat code.transect number within a site" | | site | site name, consistent with the naming scheme of the Mackenzie Valley Permanent Monitoring Plot Network (Errington et al. 2018, 2020) | | transect | within site transect identifier | | utransect | unique transect identifier in the form "site code.transect code" | | yr | measurement year | | tp | time period of measurement \[T1: 2007-8; T2: 2017-18] | | quad | within site quadrat identifier | | type | quadrat classification based on permafrost presence / absence \[CS: collapse scar - permafrost absent; PP: peat plateau - permafrost present; Trans: transitional - permafrost both present and absent in portions of the quadrat] | | Ecoclim | Ecoclimatic Region (Ecosystem Classification Group 2007, 2010) \[MB: Mid-boreal; HB: High Boreal; LS: Low Subarctic] | | Pfzone | Permafrost Zone (Heginbottom et al. 1995) \[C: Continuous; ED: Extensive Discontinuous; SD: Sporadic Discontinuous] | | dfc | distance from collapse; the point along each quadrat where peat plateau permafrost was last detected was set to a distance of dfc = 0 and the centre point of each quadrat from this “point of collapse” was calculated such that distances farther into the collapse scar were positive and distances farther into the peat plateau were negative | | tdr | volumetric soil moisture (%) | | cc | canopy cover (%) | | probe | material underlying deepest probe penetration (ice or mineral) | | maxpd | move to table 1 | | peatd | peat depth (cm) | | adjfd | adjusted frost depth (cm) | | frostd | frost depth (cm) | | probed | probe depth (cm); probe depth to frost or mineral soil | | adjwtd | adjusted water table depth (cm) | | wtd | water table depth (cm) | | shan | Shannon Diversity Index (H') | | simp | Simpson Diversity (1-D) | | rich | species richness (number of species) | | even | Pielou's Evenness (J) | | v.rich | vascular species richness (number of vascular species) | | b.rich | bryophyte species richness (number of bryophyte species) | | l.rich | lichen species richness (number of lichen species) | | m.rich | moss species richness (number of moss species) | | Lcover | lichen species richness (number of lichen species) | | Vcover | vascular species cover (%) | | Carex | *Carex* species cover (%) | | Cladina | *Cladina* species cover (%) | | Eriophorum | *Eriophorum* species cover (%) | | Sphagnum | *Sphagnum* species cover (%) | | Fcover | cover of forb species (%) | | Gcover | cover of graminoid species (%) | | Livcover | cover of liverwort species (%) | | Mcover | cover of moss species (%) | | Scover | cover of shrub species (%) | | Tcover | cover of tree species (%) | ### **Table 3: Explanation and description of fields included in “community.csv”** | **Field** | **Description** | | :----------- | :------------------------------------------------------------------------------------------------------------ | | QuadratID | unique quadrat identifier in the form "time period code.site code.quadrat code.transect number within a site" | | other fields | percent cover by species where field headings contain species codes, as explained in Table 4 | ### **Table 4: Explanation and description of fields included in “species.csv”** | **Field** | **Description** | | :--------------- | :--------------------------------------------------------------------------------------------------------------------------------- | | species code | specie code as used in table 3; typically, the first four letters of the genus followed by the first three letters of the specific epithet | | scientific name | full scientific name associated with each species code | | genus | genus | | specific epithet | specific epithet | | authority | authority associated with species name | | subspecies | subspecies | | subsp authority | authority associated with the subspecies | | type | vegetation type \[tree, shrub, forb, graminoid, moss, liverwort, or lichen] | ### **Literature Cited** * Ecosystem Classification Group. 2007. Ecological regions of the Northwest Territories – Taiga Plains. Dep. Environ. Nat. Resour., Gov. Northwest Territ., Yellowknife, NT. 173 p. plus map. * Ecosystem Classification Group. 2010. Ecological regions of the Northwest Territories – Cordillera. Dep. Environ. Nat. Resour., Gov. Northwest Territ., Yellowknife, NT. 245 p. plus map.  * Errington, R.C., S.E. Macdonald, and J.S. Bhatti. in press.  Rate of permafrost thaw and associated plant community dynamics in peatlands of northwestern Canada. Journal of Ecology * Errington, R.C., J.S. Bhatti, and E.H.Y. Li. 2018.  Mackenzie Valley Permanent Monitoring Plot Network: site locations and descriptions. Nat. Resour. Can., Can. For. Serv., North. For. Cent., Edmonton, AB.  Inf. Rep. NOR-X-426. * Errington, R.C., J.S. Bhatti, and E.H.Y. Li. 2020.  Mackenzie Valley Permanent Monitoring Plot Network: a database of stand characteristics. Nat. Resour. Can., Can. For. Serv., North. For. Cent., Edmonton, AB.  Inf. Rep. NOR-X-428. * Heginbottom, J.A., M.-A. Dubreuil, and P.A. Parker. 1995. Canada – Permafrost.  Plate 2.1 *in* National Atlas of Canada, 5th Ed. Natural Resources Canada, Ottawa, ON. (MCR 4177; scale1: 7 500 000).    * Régnière, J., R. Saint-Amant, and A. Béchard. 2014. BioSIM 10 – User’s manual. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Québec (Quebec). Information Report LAU-X-137E ## Sharing/Access information Further information and data relating to the full Mackenzie Valley Permanent Monitoring Plot Network can be found at the supplemental links in the Related Works section. ## Code/Software No code or scripts are included in this submission.