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Plant Ecology

, Volume 216, Issue 7, pp 975–988 | Cite as

On the validity of visual cover estimates for time series analyses: a case study of hummock grasslands

  • Vuong NguyenEmail author
  • Aaron C. Greenville
  • Chris R. Dickman
  • Glenda M. Wardle
Article

Abstract

Changes in vegetation cover are strongly linked to important ecological and environmental drivers such as fire, herbivory, temperature, water availability and altered land use. Reliable means of estimating vegetation cover are therefore essential for detecting and effectively managing ecosystem changes, and visual estimation methods are often used to achieve this. However, the repeatability and reliability of such monitoring is uncertain due to biases and errors in the measurements collected by observers. Here, we use two primary long-term monitoring datasets on spinifex grasslands, each established with different motivations and methods of data collection, to assess the validity of visual estimates in detecting meaningful trends. The first dataset is characterised by high spatial and temporal coverage but has limited detail and resolution, while the second is characterised by more intensive sampling but at fewer sites and over a shorter time. Using multivariate auto-regressive state-space models, we assess consistency between these datasets to analyse long-term temporal and spatial trends in spinifex cover whilst accounting for observation error. The relative sizes of these observation errors generally outweighed process, or non-observational errors, which included environmental stochasticity. Despite this, trends in the spatial dynamics of spinifex cover were consistent between the two datasets, with population dynamics being driven primarily by time since last fire rather than spatial location. Models based on our datasets also showed clear and consistent population traces. We conclude that visual cover estimates, in spite of their potential uncertainty, can be reliable provided that observation errors are accounted for.

Keywords

Monitoring Observation error Spinifex State-space models Time series Wildfire Visual cover 

Notes

Acknowledgments

We thank land managers in the Simpson Desert for access to their properties, especially Bush Heritage Australia, Bobby Tamayo and David Nelson for much logistical assistance with all aspects of the field programmes and data collection, Chin-Liang Beh and other members of the Desert Ecology Research Group for additional assistance and many volunteers for help in the field and laboratory. We also thank the Australian Research Council and the Long-Term Ecological Research Network for provision of funding.

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Vuong Nguyen
    • 1
    • 2
    Email author
  • Aaron C. Greenville
    • 1
    • 2
  • Chris R. Dickman
    • 1
    • 2
  • Glenda M. Wardle
    • 1
    • 2
  1. 1.Desert Ecology Research Group, School of Biological SciencesThe University of SydneySydneyAustralia
  2. 2.Long-Term Ecological Research Network, Terrestrial Ecosystem Research NetworkCanberraAustralia

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