Probabilistic large‐area mapping of seagrass species distributions

Abstract

ABSTRACT1. Aerial photograph classification was used to map perennial thick canopy seagrass presence/ absence over a large area (85 km 2 ) 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)).2. 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.3. Different species growth habits led to distinctive differences in large area distributions. All species were highly variable over short distances (5500 m), and spatial dependence persisted over more than 5 km. However, 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.4. 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.5. Probabilistic species mapping provided information unavailable from discretely classified maps, and facilitates targeted sampling for improving map accuracy, and for more realistically evaluating *Correspondence to: K.W. Holmes, School of Earth and Geographical Sciences, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia. E-mail: kholmes@segs.uwa.edu.au species and mixed species distribution predictions. The kriging approach, although not well suited for all types of vegetation data, performed well for clonal seagrasses.

Recovery from the impact of light reduction on the seagrass Amphibolis griffithii, insights for dredging management

“…It forms continuous monospecific meadows as well as mixed species, patchy Recovery of seagrass from light reduction 6 meadows (Holmes et al, 2007) in sandy and rocky substrates (Carruthers et al, 2007;Ducker et al, 1977). Aspects of A. griffithii's morphology and growth characteristics are important to understand in the context of predicting recovery from impact.…”

Estimation of kelp forest, Laminaria spp., distributions in coastal waters of the Shiretoko Peninsula, Hokkaido, Japan, using echosounder and geostatistical analysis

“…In recent years, integrated methods using acoustic observations via echosounder and geostatistical analyses have been suggested as practical survey and quantification methodologies for the mapping and ecological study of seagrass and seaweed beds in coastal waters [9,10]. An echosounder transmits ultrasonic sound waves through the water and continuously measures the reflections of objects (echoes) such as sea bottoms and fish schools during surveys [11].…”

“…For the estimation of horizontal distribution of kelp forests, data obtained from acoustic observation via echosounder were geostatistically interpolated using kriging [15]. Geostatistical interpolation has been applied in the past to both terrestrial and aquatic plant distribution studies [10,16]. Geostatistical interpolations can estimate the abundance of target plants with statistical references based on values such as the thickness or density of the target [17].…”

Biogeochemical processes in permeable estuarine sediments following benthic community change: implications for system dynamics & Lyngbya majuscula blooms in Deception Bay, Queensland

“…Moreton Bay is located between temperate and tropical bioregions and thus supports a unique blend of flora and fauna within a diverse range of habitat types (Davie, 2011 Community distribution maps for each detailed survey were created by interpolation of field data using indicator kriging, as used in previous studies (Holmes et al, 2007).…”

“…Phinn et al, 2008), and underwater video transects (e.g. Holmes et al, 2007). Remote sensing approaches are advantageous in that they have complete coverage over large areas.…”