Dataset for abiotic and biotic responses to woody debris additions in restored old fields in a MBACI experiment 

Experimental sites were established in the northern wheat-growing district of western Australia (Lat -29.66°, Long 116.18°) in August 2017, and monitored through to November 2019. We selected five planted old field sites with similar soil types and vegetation composition. Old fields were planted with York gum (Eucalyptus loxophleba Benth.) and dominant shrubs as understorey. At the time of sampling in 2017, vegetation age ranged from 8–13 years and distance from remnant measured 279 m (± 162 m). We established two control and two treatment plots, each measuring 5 m x 5 m, in the interrows of five planted old field sites. Both treatments were randomly assigned to plots within each site. Between August and early November 2017, we measured a total of 30 response variables at each of the control and treatment plots. Response variables included soil physical and chemical properties (bulk density, penetration resistance, soil moisture, nitrogen and carbon pools), microbial biomass, decomposition rate of roiboos and green tea as per the standardized Tea Bag Index (TBI) protocol, herbaceous vegetation cover and richness, and ant abundance and richness, as well as abundance and richness of ant functional groups. 

We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging. 

Ecological restoration of former agricultural land can improve soil condition, recover native vegetation, and provide fauna habitat. However, restoration benefits are often associated with time lags, as many attributes, such as leaf litter and coarse woody debris, need time to accumulate. Here we experimentally tested whether adding mulch and logs to restoration sites in semi-arid western Australia can accelerate restoration benefits. All sites had been cropped and then planted with native trees and shrubs (i.e., Eucalyptus, Melaleuca, Acacia spp.) 10 years prior to our experiment, to re-establish the original temperate eucalypt woodland vegetation community. We used a Multi-site Before-After / Control-Impact (MBACI) design to test the effects on 30 abiotic and biotic response variables over a period of two years. 

Experimental sites were established in the northern wheat-growing district of western Australia (Lat -29.66°, Long 116.18°) in August 2017, and monitored through to November 2019. We established two control and two treatment plots, each measuring 5 m x 5 m, in the interrows of five planted old field sites. Both treatments were randomly assigned to plots within each site. Between August and early November 2017, we measured a total of 30 response variables at each of the control and treatment plots. Response variables included soil physical and chemical properties (bulk density, penetration resistance, soil moisture, nitrogen and carbon pools), microbial biomass, decomposition rate of roiboos and green tea as per the standardized Tea Bag Index (TBI) protocol developed for comparison of litter decomposition rates across various ecosystems by Keuskamp, Dingemans, Lehtinen, Sarneel and Hefting (2013), herbaceous vegetation cover and richness, and ant abundance and richness, as well as abundance and richness of ant functional groups. Detailed sampling method descriptions are provided in the Supporting Information section. In November 2017, one treatment plot at each site was uniformly covered with 13 kg of freshly mulched York gum branches including leaves, and a second treatment plot with three York gum logs. The mulch and log application rate mimics leaf litter and fine and coarse woody debris cover of the intact York gum woodland remnants. York gum mulch was sourced from roadside tree lopping of a local shire and the logs were cut to size from recently fallen York gum branches. After two years, between August and November 2019, we re-measured all 30 response variables across the control and treatment plots