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

Nguyen, Vuong, Greenville, Aaron C., Dickman, Chris R., Wardle, Glenda M.
Plant ecology 2015 v.216 no.7 pp. 975-988
case studies, data collection, ecosystems, grasslands, herbivores, land use, models, monitoring, motivation, population dynamics, temperature, time series analysis, uncertainty, vegetation cover
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.