Five practical uses of spatial autocorrelation for studies of coral reef ecology

Hamylton, Sarah

doi:10.3354/meps10267

Cited by 12 publications

(8 citation statements)

References 44 publications

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“…Similar predictive modelling approaches may also be applied to generate surfaces of sediment properties (e.g., Reference [97]) or benthic diversity metrics (e.g., Reference [10]). While detailed analyses of the predictive modelling were outside the scope of this study, our results highlighted common challenges of predictive mapping in marine environments including accounting for spatial autocorrelation in subsampling design [98]. Our findings showed the towed underwater video data exhibited spatial autocorrelation within the subsampling intervals tested, however the detection of spatial autocorrelation in this case was balanced with the need to maintain a viable calibration and validation dataset.…”

Section: South Solitary Islands Case Study Areamentioning

confidence: 71%

Techniques for Classifying Seabed Morphology and Composition on a Subtropical-Temperate Continental Shelf

et al. 2019

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In 2017, the New South Wales (NSW) Office of Environment and Heritage (OEH) initiated a state-wide mapping program, SeaBed NSW, which systematically acquires high-resolution (2–5 m cell size) multibeam echosounder (MBES) and marine LiDAR data along more than 2000 km of the subtropical-to-temperate southeast Australian continental shelf. This program considerably expands upon existing efforts by OEH to date, which have mapped approximately 15% of NSW waters with these technologies. The delivery of high volumes of new data, together with the vast repository of existing data, highlights the need for a standardised, automated approach to classify seabed data. Here we present a methodological approach with new procedures to semi-automate the classification of high-resolution bathymetry and intensity (backscatter and reflectivity) data into a suite of data products including classifications of seabed morphology (landforms) and composition (substrates, habitats, geomorphology). These methodologies are applied to two case study areas representing newer (Wollongong, NSW) and older (South Solitary Islands, NSW) MBES datasets to assess the transferability of classification techniques across input data of varied quality. The suite of seabed classifications produced by this study provide fundamental baseline data on seabed shape, complexity, and composition which will inform regional risk assessments and provide insights into biodiversity and geodiversity.

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Section: South Solitary Islands Case Study Areamentioning

confidence: 71%

Techniques for Classifying Seabed Morphology and Composition on a Subtropical-Temperate Continental Shelf

et al. 2019

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“…This is a useful indicator of the spatial dimensions of potentially influencing ecological processes, e.g. predation, competition or reproduction (Hamylton 2013), and may be detectable as the statistically significant presence (or absence) of similarity between point samples of population data. To elucidate directional influences within populations, semivariograms can be constructed in which point samples of assemblage metrics (e.g.…”

Section: Statistical Analysesmentioning

confidence: 99%

On the very edge: faunal and functional responses to the interface between benthic seagrass and unvegetated sand assemblages

Barnes¹,

Hamylton²

2016

Mar. Ecol. Prog. Ser.

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Changes in macrobenthic spatial structure were investigated across the precise 0.5 m wide boundary zone between intertidal seagrass and unvegetated sand in Moreton Bay, Queensland, Australia. Faunal abundance and species density in the marginal seagrass were only slightly reduced relative to local non-boundary areas. Although gradual diminution in seagrass faunal abundance occurred towards the interface and a few locally dominant species were absent from the boundary zone, most transition was far from gradual, instead being exactly coincident with the specific 0.1 m interface between the 2 habitats. Over this distance, faunal abundance and species density fell by 56 to 60% (from 1412 to 625 m −2 and from an estimated 42 species per sampling horizon to 17, respectively). Dominant seagrass species were abundant right up to the interface itself, but their densities had fallen by 82% a further 0.1 m into the adjacent sand. Functional-group density and diversity mirrored these changes, decreasing over the same distance by 30 to 36%. Spatial analysis showed that adjacent cores along lattice sampling horizons, although spaced further apart, were more similar to each other than were those oriented across horizons, even though these were contiguous. Characteristic high animal species richness in the seagrass relative to adjacent sand appears to be not so much related to characteristics of the seagrass habitat as a whole rather than to the presence or absence of individual plants. Asymmetrically, distributions of species characteristic of the sand stopped somewhat in advance of the interface, leaving a marginal sand zone dominated by generalists occurring at equal (low) species and population density across both habitats.

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“…Autoregressive models have successfully explained observable trends in reef benthic community ecology (Hamylton, , ; Hamylton et al ., ) and reef island structure (Hamylton and Puotinen, ). In this study, two autoregressive equations were applied to model the area and volume ( μ = dependent variables) of each island as a function of the local environmental conditions captured by the explanatory variables.…”

Section: Methodsmentioning

confidence: 99%

A meta‐analysis of reef island response to environmental change on the Great Barrier Reef

Hamylton

Puotinen

2015

Earth Surf Processes Landf

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Reef islands on the Great Barrier Reef are influenced by a range of environmental factors. A meta-analysis of 103 islands is presented to express variation in island size (area and volume) as a function of latitudinal and cross shelf gradients in regional oceanographic factors (exposure to incident waves, tidal range and tropical cyclone frequency) and local physical factors (position on the shelf, area, length and depth of supporting reef platform, vegetative cover). Models performed well for unvegetated sandcays (R 2 = 0.89), vegetated sandcays (R 2 = 0·72) and low wooded islands (R 2 = 0.78), with a moderate level of variation explained when all islands were simultaneously regressed (R 2 = 0.58). Future island dynamics were simulated for anticipated changes in cyclone regime, wave activity and sea level. For 38 islands mapped on the 1973 Royal Society and Universities of Queensland Expedition to the Northern Great Barrier Reef, change over the same 22 year period was determined and the relative magnitude of observed and modelled changes was compared and found to be consistent through rank correlation analysis (Γ = 0.84 for unvegetated sandcays, Γ = 0.81 for vegetated sandcays). Simulations of island area or volume change from 2000 to 2100 indicated that under a 30% decrease in tropical cyclone activity, unvegetated sandcays continue to accrete at a lower rate, whereas all island types erode under a 38% increase in tropical cyclone activity. Vegetated sandcays initially accrete at higher levels of cyclone activity, entering an erosive state with a 60% increase in activity. Low wooded islands are unresponsive to environmental changes modelled. A sensitivity analysis of vegetated and unvegetated sandcays indicated that the presence of vegetation increases the tropical cyclone activity threshold at which islands begin to erode. Greatest sedimentary losses occur within the central band of high cyclone activity between Cooktown and Mackay.

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Five practical uses of spatial autocorrelation for studies of coral reef ecology

Cited by 12 publications

References 44 publications

Techniques for Classifying Seabed Morphology and Composition on a Subtropical-Temperate Continental Shelf

Techniques for Classifying Seabed Morphology and Composition on a Subtropical-Temperate Continental Shelf

On the very edge: faunal and functional responses to the interface between benthic seagrass and unvegetated sand assemblages

A meta‐analysis of reef island response to environmental change on the Great Barrier Reef

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