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Data collection for fresh-water regions of The Ecosystem Health Monitoring Program (EHMP), in southeast Queensland, Australia, involves the sampling of over 130 sites among 19 catchments twice per year and has been ongoing for over ten years. The sampling design was derived following an exhaustive process of indicator and site selection to develop a composite indicator that represented aquatic ecosystem health. After 13 years of implementation, there was an interest in identifying redundancies in sampling to reduce sampling costs without making a substantial impact on the integrity of the program and its capacity to report on ecosystem health. This paper focuses on identifying a subset of sites and times that could be removed from sampling with a minimal impact on the subsequent ecosystem health scores. Herein, Mixed models are employed to assess a variance structure from which optimality criteria are utilized to identify the scheme. Integer programs are then used to ensure specific practical constraints are observed. [ABSTRACT FROM AUTHOR]

Understanding the determinants of species' distributions and abundances is a central theme in ecology. The development of statistical models to achieve this has a long history and the notion that the model should closely reflect underlying scientific understanding has encouraged ecologists to adopt complex statistical methods as they arise. In this paper we describe a Bayesian hierarchical model that reflects a conceptual ecological model of multi-scaled environmental determinants of riverine fish species' distributions and abundances. We illustrate this with distribution and abundance data of a small-bodied fish species, the Empire gudgeon Hypseleotris galii, in the Mary and Albert Rivers, Queensland, Australia. Specifically, the model sought to address; 1) the extent that landscape-scale abiotic variables can explain the species' distribution compared to local-scale variables, 2) how local-scale abiotic variables can explain species' abundances, and 3) how are these local-scale relationships mediated by landscape-scale variables. Overall, the model accounted for around 60% of variation in the distribution and abundance of H. galii. The findings show that the landscape-scale variables explain much of the distribution of the species; however, there was considerable improvement in estimating the species' distribution with the addition of local-scale variables. There were many strong relationships between abundance and local-scale abiotic variables; however, several of these relationships were mediated by some of the landscape-scale variables. The extent of spatial autocorrelation in the data was relatively low compared to the distances among sampling reaches. Our findings exemplify that Bayesian statistical modelling provides a robust framework for statistical modelling that reflects our ecological understanding. This allows ecologists to address a range of ecological questions with a single unified probability model rather than a series of disconnected analyses. [ABSTRACT FROM AUTHOR]