Abstract
Historically, marine ecologists have lacked efficient tools that are capable of capturing detailed species distribution data over large areas. Emerging technologies such as high‐resolution imaging and associated machine‐learning image‐scoring software are providing new tools to map species over large areas in the ocean. Here, we combine a novel diver propulsion vehicle (DPV) imaging system with free‐to‐use machine‐learning software to semi‐automatically generate dense and widespread abundance records of a habitat‐forming algae over ~5,000 m2 of temperate reef. We employ replicable spatial techniques to test the effectiveness of traditional diver‐based sampling, and better understand the distribution and spatial arrangement of one key algal species. We found that the effectiveness of a traditional survey depended on the level of spatial structuring, and generally 10–20 transects (50 × 1 m) were required to obtain reliable results. This represents 2–20 times greater replication than have been collected in previous studies. Furthermore, we demonstrate the usefulness of fine‐resolution distribution modeling for understanding patterns in canopy algae cover at multiple spatial scales, and discuss applications to other marine habitats. Our analyses demonstrate that semi‐automated methods of data gathering and processing provide more accurate results than traditional methods for describing habitat structure at seascape scales, and therefore represent vastly improved techniques for understanding and managing marine seascapes.
Highlights
Healthy coastal and estuarine ecosystems have high social, economical, economic and environmental value, yet continue to suffer alarming degradation (Connell et al, 2008; Lotze et al, 2006)
To avoid errors associated with site-level variation in spatial autocorrelation (Diniz-F ilho, Bini, & Hawkins, 2003), hierarchical designs have been the backbone of marine ecology, whereby variance can be partitioned at multiple levels (Underwood, Chapman, & Connell, 2000)
We modeled the probability of kelp cover over the Sydney Harbour study sites as a function of latitude (LAT), longitude (LONG), bathymetry (BATH), and the extent of viable rocky substrate (RREEF; from existing qualitative habitat maps; Creese et al, 2009)
Summary
To avoid errors associated with site-level variation in spatial autocorrelation (Diniz-F ilho, Bini, & Hawkins, 2003), hierarchical designs have been the backbone of marine ecology, whereby variance can be partitioned at multiple levels (Underwood, Chapman, & Connell, 2000). These designs held clear advantages for hypothesis testing, and alternative methods to investigate and account for variability across spatial scales (e.g., Stevens & Olsen, 2004) were inappropriate for large-scale subtidal implementation due to a lack of efficient subtidal positioning systems.
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