DOI: 10.5061/dryad.f7m0cfz0z
title: Dataset for: Population decline in a Pleistocene refugium: stepwise, drought-related dieback of a South Australian eucalypt

description: Refugia can facilitate the persistence of species through long-term
 environmental change, but it is not clear if Pleistocene refugia will
 remain functional under anthropogenic climate change. Dieback of species
 within refugia therefore raises concerns about their long-term
 persistence. Using repeat field surveys, we investigate dieback patterns
 of an isolated population of Eucalyptus macrorhyncha during two droughts
 and discuss prospects for its continued persistence in a Pleistocene
 refugium. We first confirm that the Clare Valley in South Australia has
 constituted a long-term refugium for the species, with the population
 being genetically highly distinct from other conspecific populations.
 However, the population lost &gt;40% of individuals and biomass
 through the two droughts, with mortality being just below 20% after the
 Millennium Drought (2000–2009) and almost 25% after the Big Dry
 (2017-2019). The best predictors of mortality differed after each drought.
 While the north-facing aspect of a sampling location was a significant
 positive predictor after both droughts, biomass density and slope were
 significant negative predictors only after the Millennium Drought, and
 distance to the north-west corner of the park, which intercepts hot, dry
 winds, was significant after the Big Dry only. This suggests that more
 marginal sites with low biomass and located on exposed, flat plateaus were
 more vulnerable initially, but that heat-stress was an important driver of
 dieback during the Big Dry. Therefore, the causative drivers of dieback
 may change during population decline. Regeneration occurred predominantly
 on southern and eastern aspects, which would receive the least solar
 radiation. Occurrence in a refugium did not protect this population from
 dieback. However, gullies with lower solar radiation are continuing to
 support relatively healthy, regenerating stands of red stringybark,
 providing hope for persistence in small pockets. Monitoring and managing
 these pockets during future droughts will be essential to ensure the
 persistence of this isolated and genetically unique population.
description: The data contains three datasets derived from analysing data from
 multiple surveys of a red stringybark population
 (<em>Eucalyptus</em> <em>macrorhyncha</em>) in
 Spring Gully Conservation Park (SGCP), Clare Valley, Australia. These are
 the Tree Health Index (THI), Biomass and Drivers datasets, which are used
 in the analyses of the associated paper. Below I explain how each dataset
 was obtained. The South Australian Department of
 Environment and Water (DEW) initiated a tree health monitoring program in
 2009, during which four North-South oriented transects were established
 in SGCP. Each transect (between 1.2 and 1.8 km long)
 had sampling sites every 50 m. At each sampling site, the four closest
 canopy trees within a 10 m radius were marked with a permanent aluminum
 tag, their location recorded with a handheld GPS (brand and model
 unknown), and various measurements relating to their health status taken
 (see below). In total, 471 trees were surveyed, 30 of which were South
 Australian blue gums (<em>Eucalyptus</em>
 <em>leucoxylon</em> F.Muell.) and the remainder were red
 stringbark. Transects were surveyed in January and February 2009, March 2010,
 November 2011, August 2012, November 2013, and September 2014. Parameters
 recorded included tree status (dead/ alive; trees with dead stems but with
 living basal sprouts were scored as alive), crown extent (percentage area
 of assessable crown with live leaves), and crown density (percentage of
 skylight blocked by the leafy crown). Percentage values were recorded as
 eight categories: 0 (0%), 1 (1–10%), 2 (11–20%), 3 (21–40%), 4 (41–60%), 5
 (61–80%), 6 (81–90%), and 7 (91–100%). The assessable crown was defined as
 consisting of all living and dead branches of the crown.
 In addition, epicormic
 growth and extent of reproductive activity (presence of flowers and/or
 fruits) were classified into four categories: 0 (absent, not visible), 1
 (scarce, present but not readily visible), 2 (common, clearly visible
 throughout the assessable crown), 3 (abundant, dominates the appearance of
 the assessable crown). We calculated a summative index consisting of canopy extent,
 canopy density, and epicormic growth to indicate tree health – hereafter
 referred to as the tree health index (THI). Because crown extent and density are considered
 the most important indicators of tree health, we retained them at their larger scale (0–7,
 compared to 0–3 for epicormic growth), giving a maximum value of 17 for
 the THI. Trees that appeared dead at some surveys but that later
 resprouted (i.e., epicormic growth or basal sprouts), were retrospectively
 awarded a THI score of 1 (instead of zero). To get an
 indication of the health status of the red stringybark population, the
 proportion of dead trees and the average THI of all 441 stringybark trees
 surveyed repeatedly since 2009 were determined and this data is available
 in the THI dataset. In September and December 2021, we revisited all
 trees that had been surveyed and tagged previously. Relocation of trees
 was achieved with high confidence because of the availability of GPS
 locations for each tree and because tags remained on, or had fallen
 directly beneath, at least two of the four trees at each site. Six sites
 consisting entirely of blue gum were not resurveyed (sites T1S02, T1S03,
 T1S04, T1S05, T1S30, T3S09). Methods for the resurvey focused on
 replicating the methods used in the earlier surveys (to facilitate
 comparisons) and on collecting additional information to provide
 area-based estimates of dieback. To achieve area-based estimates, we determined a
 center point for each site so that each of the four trees at a site was in
 a different quarter (delineated using the four cardinal directions). For
 sites with one or more blue gum trees among the four surveyed trees, blue
 gums were replaced by the nearest stringybark in the relevant quarter. In
 one instance, no nearest stringybark neighbour was present within 10 m and
 this site was excluded from analyses including biomass density. Additional
 trees were added to four sites that had less than four surveyed trees.
 This resulted in a total of 112 sites with 448 trees of red stringybark.
 This allowed estimating the tree density per hectare at each site by
 measuring, averaging, and squaring the distance of each tree to the center
 point. The inverse of this average distance was then multiplied by the
 value of the desired area (in this case 1 ha) to obtain an estimate of
 tree density, following the point-centered quarter
 method. To estimate biomass, we recorded diameter at
 breast height (DBH) and tree height for each stem of a tree (trees
 regularly had multiple stems), living or dead. A tree with dead stems was
 considered alive if there was any epicormic or basal growth present. A
 stem was considered alive if epicormic growth was present above 1.3m in
 height. The height (Ht) was estimated to the nearest meter using a 1.5 m
 range pole that was held up vertically overhead to provide a reference of
 approximately 3.5 m height. The DBH was measured using a diameter tape 1.3
 m above the ground. A wood density (WD) of 795 (± 19) kg.m<sup>-3</sup> was assumed
 for all trees. We used
 these values to calculated the above-ground biomass (AGB)
 as: AGB = 0.0673
 × (WD × DBH<sup>2</sup>
 × Ht)<sup>0.976</sup>. AGB was
 determined for every stem and then aggregated per tree, meaning a single
 individual could be composed of both living and dead biomass.
 We multiplied the estimate
 of number of individuals per hectare by the mean AGB per tree to obtain
 area-based estimates of biomass, i.e., biomass density. These calculations
 were done for each site (to obtain estimates of biomass density per site)
 and for all 112 sites combined (to obtain a parkwide estimate) and this
 data is available in the biomass dataset.
 As an estimate of
 regeneration, the occurrence of seedlings (&lt; 1 m tall, woody growth
 lacking) and saplings (&lt; 1 m tall, woody growth present) within a 3
 m radius of the center point was recorded. Seedling and sampling numbers
 for each site were combined to provide an indicator of recruitment. In
 addition, aspect (in degrees rounded to 10° intervals and determined with a compass) and
 slope (in degrees using a clinometer) were recorded for each site. We
 calculated ‘northness’ and ‘eastness’ as the cosine and sine of the aspect
 (in radians), respectively. Where trees within a site were located on
 different slopes in a valley, the aspect and slope were recorded for each
 slope and then averaged. Distance to the north-west corner of the park
 (the area most affected by hot, dry summer winds) was calculated as
 the planar distance between this location and the sampling locations using
 the “<em>Near</em>
 (<em>Analysis</em>)” geoprocessing tool in ArcGIS
 Pro. We calculated the proportion of dead trees per
 site in 2011 (Mortality 2011) and 2021 (Mortality 2021) and regeneration
 as indicators of dieback and persistence (response variables). These
 variables for the 112 sites of the 2021 survey, but including only trees
 that were surveyed in 2011 as well (a total of 441 trees) are
 presented in the Drivers dataset. 
description: # Dataset for: "Population decline in a Pleistocene refugium:
 stepwise, drought-related dieback of a South Australian eucalypt" ***
 The data contains three datasets derived from analysing data from multiple
 surveys of a red stringybark population (*Eucalyptus macrorhyncha*) in
 Spring Gully Conservation Park (SGCP), Clare Valley, Australia. These are
 the Tree Health Index (THI), Biomass and Drivers datasets, which are used
 in the analyses of the associated paper. The Methods section under
 'Data Description' explains how each dataset was obtained. ##
 Description of the Data and file structure **Biomass dataset**: Excel
 spreadsheet with 3 sheets (formulas have been retained). * Sheet 1: for
 each surveyed stem it has the ID number applied to the site, tree and stem
 and the following information: treeDead (is the tree dead (d) or alive
 (a), stemDead (is the stem dead (d) or alive (a), D (diameter at breast
 height, in cm), H (tree height, in m), and estimates of wood density (WD)
 and above-ground biomass (AGB) for each stem for mean, high and low
 estimate of wood density. * Sheet 2: uses the data from sheet 1 to
 calculate totals for each tree - it divides the biomass into living (AGBa)
 and dead (AGBd) biomass. Also included are the diameter of the largest
 stem (Dmax) and the number of stems (StemNo) for each tree. * Sheet 3:
 uses the data from sheet 2 to calculate totals for each site. Also
 included are the longitude (long) and latitude (lat) of each site, the
 proportion of dead trees (DeadProp), the height of the tallest tree (Hmax,
 in metres), the average crown extent (CrownExtAvg, on a scale of 0-7 see
 'Methods' under 'Data Description'), the average crown
 density (CrownDensAvg, on a scale of 0-7 see 'Methods' under
 'Data Description'), the average epicormic growth (EpicormicAvg,
 on a scale of 0-3 see 'Methods' under 'Data
 Description'), the total number of saplings and seedlings recorded
 (Regrowth), the average area (Area, in square metres), the tree density
 (TreeDensity), the biomass density (BiomassDens, in tons/ha), and the
 aspect and slope (in degrees) recorded. **DriversDataset**: Excel
 spreadsheet with the variables used in modelling: site ID number (site
 ID), mortality rate in 2011 (Mort2011) and 2021 (Mort2021), the average
 number of stems per tree (AvgStemNo),
 Regrowth/TreeDensity/BiomassDens/Slope (see descriptions for sheet 3 in
 Biomass dataset), distance to the north-west corner (dNW), northness
 (North) and Eastness (East) **THI dataset**: includes the tree health
 index (THI) and status for each tree in each survey (2009, 2010, 2011,
 2012, 2013, 2014, 2021) ## Sharing/access Information NA