Dataset: Developmental trajectories of forest recovery from passive regrowth and active planting on the Atherton uplands derived from chronosequence data. (NERP TE 12.2, GU and UQ)


Description

This data set describes change in forest attributes over time in response to passive regrowth and biodiverse ecological restoration plantings across the southern Atherton uplands. These data were obtained from four sets of spatially replicated site-types: (1) a chronosequence of 29 passive regrowth sites (1-67 years) on previously forested land with a subsequent period of land use for agriculture (e.g. cropping or grazing); (2) a comparable chronosequence of 25 actively planted sites (1-25 years); (3) five pasture reference sites, indicating the pre-reforestation condition; and (4) eight rainforest reference sites indicating the target of reforestation. The reference sites provide a context for interpreting the developmental trajectories of reforested sites. Development trajectories are measured using a range of structural and floristic attributes.

The primary purpose for collecting this data was to:
(1) evaluate time lags and uncertainty of recovery for different elements of tropical forest (i.e. structure, biomass, species density and community composition) through passive regrowth and active planting;
(2) identify forest attributes that are particularly slow to develop under either approach;
(3) explore ecological explanations for the later that might suggest barriers to recovery and inform modifications to current restoration practices to catalyse outcomes.

The study was conducted in wet tropics uplands of the south-central Atherton Tableland, north eastern Australia (17°10´ ¿ 17°35´S, 145°30´ ¿ 145°45´E). Field surveys in ecological restoration plantings and reference sites (pasture and forest) were completed between May 2008 and August 2009. Comparative assessments of regrowth sites were conducted between October 2012 and June 2013.

Methods:

Vegetation structure and floristic composition at each site were measured within two 50 x 20 m transects (combined for analysis as one 100 m transect), using an established monitoring protocol (Kanowski et al. 2010).

Four types of structural attribute were quantified: canopy cover, canopy height, wood volume and woody stem density. Canopy cover (%) and height (m) were averaged across six 10 x 10 m quadrats, centred 5, 25, 45, 55, 75 and 95 m along the transect. Stems of all trees and shrubs (free-standing woody-stemmed plants, henceforth ¿TS¿) >1.0 m tall were counted in variable-width transects, according to the following dbh categories (cm): <2.5, 2.5-5, 5-10, 10-20, 20-30, 30-40, 40-50, 50-75, 75-100, >100. Transect widths were 5, 10 and 20 m respectively for stem dbh categories <10, 10-50, and >50 cm, giving respective areas surveyed of 500, 1,000, and 2,000 m2.

Presence of special life forms (i.e. vines, epiphytes and ferns), canopy cover and canopy height were assessed within three 10 x 10 m quadrats centred on the 5, 25 and 45 m points of each transect. Aboveground biomass of live trees and shrubs was calculated from site-specific stem data together with assumed average wood densities from Kanowski et al. (2010b).

Data on forest attributes were pooled for each site. Estimates of stem density and species density of freestanding trees and shrubs were further partitioned according to species origin, dispersal mode and diaspore size (seeds plus any additional tissues that assist in dispersal) and membership to near-basal plant families. Origin was simply native or non-native. We considered separately species dependent on either of two important dispersal vectors. These were ¿wind dispersed¿ and ¿bird or bat dispersed¿. The later did not include non-volant (non-flying) vertebrates and invertebrates such as cassowary, musky-rat kangaroo, rodents, pigs and ants. For animal dispersed plant taxa, we further differentiated between those species with small diaspores (< 10 mm) and moderate to large diaspores (> 10 mm) to reflect important variation in frugivore capacity (Moran & Catterall 2010). Near-basal plant families followed the assessment of Metcalfe and Ford (2009) derived from current molecular phylogeny of angiosperms. This focus on plant families with ancient origins is in recognition of their importance to the ¿outstanding universal value¿ of the Wet Tropics World Heritage Area and is relevant to ongoing management targeted at conserving evolutionary history.

Further details can be found in this publication:
SHOO, L. P., FREEBODY, K., KANOWSKI, J., & CATTERALL, C. P. (2015). Slow recovery of tropical old field rainforest regrowth and the value and limitations of active restoration. Conservation Biology. DOI: 10.1111/cobi.12606

References:

Kanowski, J., C. P. Catterall, K. Freebody, A. N. D. Freeman, and D. A. Harrison 2010a. Monitoring Revegetation Projects in Rainforest Landscapes. Toolkit Version 3. Reef and Rainforest Research Centre Ltd., Cairns. http://www.rrrc.org.au/publications/biodiversity_monitoring3.html.

Kanowski, J. and Catterall, C.P. 2010b. Carbon stocks in above-ground biomass of monoculture and mixed species plantations and environmental restoration plantings in north-east Australia. Ecological Management and Restoration 11: 119-126.

Metcalfe, D. J., and A. J. Ford. 2009. A re-evaluation of Queensland's Wet Tropics based on 'primitive' plants. Pacific Conservation Biology 15:80-86.

Moran, C., and C. P. Catterall. 2010. Can functional traits predict ecological interactions? A case study using rain forest frugivores and plants in Australia. Biotropica 42:318-326.

Limitations:

Biomass calculations were based on certain assumptions, and should therefore be interpreted as broad estimates rather than accurate values. However, they do represent more accurate estimates of relative above ground wood volume.

Format:

An XLSX file (21 MB), Catterall Shoo et al Data extract on developmental trajectories of forest recovery.xls, containing containing two (2) spreadsheets; data and metadata. The metadata sheet contains units and descriptions for the columns in the data spreadsheet.

Data Dictionary:

General Information

Distributions