Determination of the physical drivers of Zostera seagrass distribution using a spatial autoregressive lag model
A. J. Hirst A E , K. Giri B , D. Ball C and R. S. Lee D
+ Author Affiliations
- Author Affiliations
A Centre for Integrated Ecology, School of Life and Environmental Science, Deakin University, Vic. 3220, Australia.
B Department of Economic Development, Jobs, Transport and Resources, Vic. 3053, Australia.
C PO Box 457, Frankston, Vic. 3199, Australia.
D Marine Water Environmental Solutions, Environment Protection Authority, Macleod, Vic. 3086, Australia.
E Corresponding author. Email: a.hirst@deakin.edu.au
Marine and Freshwater Research 68(9) 1752-1763 https://doi.org/10.1071/MF16252
Submitted: 12 July 2016 Accepted: 15 December 2016 Published: 15 February 2017
Abstract
Seagrass mapping has become a key tool in understanding the causes of change in seagrass habitats. The present study demonstrates a method for examining relationships between seagrass habitat polygons and environmental data generated by hydrodynamic, wave, catchment and dispersion models. Seagrass abundance data are highly spatially autocorrelated and this effect was corrected using a spatially simultaneous autoregressive lag model (SSARLM). The physical processes that determine the spatial distribution of seagrass in Port Phillip Bay, Australia, were investigated by examining the links between seagrass distribution and abundance and broadscale hydrodynamic (waves, currents), physical (light, depth, salinity and temperature) and catchment (nutrient and suspended sediment concentrations) processes. The SSARLM indicated that the distribution of Zostera spp. meadows is principally constrained by two physical thresholds, namely, wave height or exposure and light. The former excludes seagrasses from colonising wave-exposed coastlines, whereas the latter directly determines the depth profile of seagrasses through its influence on light availability. In total, 95% of all seagrass occurred within grid cells with a mean significant wave height of <0.38 m and a mean percentage irradiance of >33% surface levels. By comparison, variation in water quality, represented by variables such as modelled total nitrogen, suspended solids or salinity, had little influence on seagrass distribution.
Additional keywords: hydrodynamic, wave and dispersion models, Port Phillip Bay, seagrass mapping, spdep package, wave and light thresholds.
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