Long-term field-based monitoring is essential to develop a deep understanding of how ecosystems function and to identify species at risk of decline. However, conducting long-term field-based research poses some unique challenges due to the harsh environmental conditions or extreme weather events that may be encountered. Such conditions are especially likely to occur in arid environments. Fieldwork issues can arise from vehicle breakdowns, wildfires and heavy rainfall events, all of which can delay or even cancel data collection. In addition, long-term monitoring typically requires multiple observers, which may add observation bias to estimates of measured parameters. Thus there is an increasing need to develop new statistical techniques that take advantage of the power of long time-series datasets that also are incomplete. Here we discuss multivariate autoregressive state-space (MARSS) modelling; a relatively new statistical technique for modelling long-term time-series data. MARSS models allow users to investigate incomplete datasets caused by missing values. In contrast to traditional modelling techniques, such as generalised linear models that only estimate error from environmental stochasticity (process error), MARSS models estimate both process and observation errors. By estimating observation errors, researchers can incorporate bias from different observers and methods into population or other parameter estimates. To illustrate the MARSS technique we interrogate long-term animal and plant datasets from arid central Australia that contain missing values and were collected by multiple observers. We then discuss the findings from the MARSS models and their implications for management. Lastly, we suggest future applications that this technique could be used for, such as studies of animal movements and food webs.

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