Volume 56, Issue 3

A comparison of spatially explicit landscape representation methods and their relationship to stream condition

ERIN E. PETERSON

CSIRO Division of Mathematics, Informatics and Statistics, Indooroopilly, Qld, Australia

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FRAN SHELDON

Australian Rivers Institute, Griffith University, Nathan, Qld, Australia

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ROSS DARNELL

CSIRO Division of Mathematics, Informatics and Statistics, Indooroopilly, Qld, Australia

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STUART E. BUNN

Australian Rivers Institute, Griffith University, Nathan, Qld, Australia

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BRONWYN D. HARCH

CSIRO Division of Mathematics, Informatics and Statistics, Indooroopilly, Qld, Australia

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First published: 30 September 2010
Citations: 58
Erin E. Peterson, CSIRO Division of Mathematics, Informatics and Statistics, 120 Meiers Rd., Indooroopilly, Qld 4068, Australia. E‐mail: erin.peterson@csiro.au

Summary

1. Biodiversity, water quality and ecosystem processes in streams are known to be influenced by the terrestrial landscape over a range of spatial and temporal scales. Lumped attributes (i.e. per cent land use) are often used to characterise the condition of the catchment; however, they are not spatially explicit and do not account for the disproportionate influence of land located near the stream or connected by overland flow.

2. We compared seven landscape representation metrics to determine whether accounting for the spatial proximity and hydrological effects of land use can be used to account for additional variability in indicators of stream ecosystem health. The landscape metrics included the following: a lumped metric, four inverse‐distance‐weighted (IDW) metrics based on distance to the stream or survey site and two modified IDW metrics that also accounted for the level of hydrologic activity (HA‐IDW). Ecosystem health data were obtained from the Ecological Health Monitoring Programme in Southeast Queensland, Australia and included measures of fish, invertebrates, physicochemistry and nutrients collected during two seasons over 4 years. Linear models were fitted to the stream indicators and landscape metrics, by season, and compared using an information‐theoretic approach.

3. Although no single metric was most suitable for modelling all stream indicators, lumped metrics rarely performed as well as other metric types. Metrics based on proximity to the stream (IDW and HA‐IDW) were more suitable for modelling fish indicators, while the HA‐IDW metric based on proximity to the survey site generally outperformed others for invertebrates, irrespective of season. There was consistent support for metrics based on proximity to the survey site (IDW or HA‐IDW) for all physicochemical indicators during the dry season, while a HA‐IDW metric based on proximity to the stream was suitable for five of the six physicochemical indicators in the post‐wet season. Only one nutrient indicator was tested and results showed that catchment area had a significant effect on the relationship between land use metrics and algal stable isotope ratios in both seasons.

4. Spatially explicit methods of landscape representation can clearly improve the predictive ability of many empirical models currently used to study the relationship between landscape, habitat and stream condition. A comparison of different metrics may provide clues about causal pathways and mechanistic processes behind correlative relationships and could be used to target restoration efforts strategically.

Number of times cited according to CrossRef: 58

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