Ecological Indicators (Nov 2024)

Quantifying environmental drivers of vegetation condition in a temperate ecosystem can improve detection of management impacts

  • Johanna G. Kuhne,
  • Patrick J. O’Connor,
  • Jasmin G. Packer,
  • Thomas A.A. Prowse

Journal volume & issue
Vol. 168
p. 112783

Abstract

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Disentangling the effects of environmental variation and management actions on vegetation condition is increasingly important given increasing efforts to tackle biodiversity loss and the advent of environmental accounting programs. The Mount Lofty Ranges (South Australia) contains temperate ecosystems supporting rich but threatened biodiversity. Using 15 years of vegetation monitoring, we quantified drivers of and trends in four indicators of vegetation health; native species richness, vegetation structure, regeneration, tree habitat quality, and two indicators of vegetation threats; grazing pressure and weed species richness. After correcting for differences between vegetation communities, we found all indicators were significantly associated with environmental variables. Seasonal effects were found for native and weed species richness and vegetation structure with peaks in spring. Significant spatial effects for native and weed species richness, vegetation structure and grazing scores reflect historic and current land use. Rainfall in the year before a survey resulted in higher native and weed species richness and higher grazing scores. To demonstrate the application of model-based correction factors when monitoring vegetation change in this system, we simulated a management-induced native species gain and tested the capacity of different before-after survey regimes to detect this gain under environmental variability. Across sites, model-based corrections increased the probability of detecting the simulated gain by c. 8% and reduced the variance in this probability approximately six-fold. Our results quantify the effects of environmental drivers on vegetation in the study region and highlight the improved capacity to detect the true effects of management actions through model-based adjustments for environmental drivers.

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