Probabilistic large‐area mapping of seagrass species distributions

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Abstract
Aerial photograph classification was used to map perennial thick canopy seagrass presence/absence over a large area (85 km2) off the coast of Western Australia. Within those areas mapped as seagrass, a geostatistical nonparametric interpolation method was applied to map the probability of seagrass species presence from underwater tow video. Multiple species mixtures were mapped at fixed probability thresholds of 0.95, 0.75, 0.50, and 0.25. Taxa included Amphibolis spp., Posidonia coriacea, P. sinuosa, P. australis and ephemeral species (Halophila and Zostera tasmanica (newly named as Heterozostera polychlamys)). The most commonly occurring species were respectively Amphibolis spp., Posidonia coriacea, P. sinuosa, P. australis, and the ephemeral species. Amphibolis, P. coriacea, and the ephemeral species were mapped predominantly as mixed assemblages (71–89% mixed), whereas P. sinuosa and P. australis were typically mapped as single species. Different species growth habits led to distinctive differences in large area distributions. All species were highly variable over short distances (Posidonia sinuosa meadows were oriented with the longest axis running north–south, and a shorter axis running east–west perpendicular to the coastline (spatial dependence to 2.8 km and 0.8 km, respectively). The ephemeral species were less successfully mapped, largely owing to the potentially different growth patterns of the grouped species, and because their full extent could not be captured by the aerial photograph classification. The individual biology of each species results in unique landscape features where Posidonia sinuosa forms larger continuous and predominantly monospecific meadows, whereas the more common Amphibolis and P. coriacea form multi‐species patchy meadows. These mapped features suggest that the emergence of species patterns in seagrass landscapes is influenced by differences in clonal growth among seagrass species. Probabilistic species mapping provided information unavailable from discretely classified maps, and facilitates targeted sampling for improving map accuracy, and for more realistically evaluating species and mixed species distribution predictions. The kriging approach, although not well suited for all types of vegetation data, performed well for clonal seagrasses. Copyright © 2006 John Wiley & Sons, Ltd.

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