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- Materials and methods
The requirement to protect marine habitats and biodiversity is articulated in national, regional and international guidelines and policies [e.g. Habitat Directive (EC 1992), Magnuson-Stevens Fishery Conservation and Management Act in the US (U.S. Congress 1996) and the Convention on Biological Diversity (UNEP/CBD 2010)]. To support such guidelines and policies, both the distribution and status of different habitat types have to be described, monitored and reported. This information is used to assess human impacts and to determine the need for, and performance of, management measures.
The distribution and status of habitat can be described from the identity and abundance of component species, but the approach can be too costly and time-consuming to apply at large spatial scales. However, to meet many operational needs, information on habitat complexity may be sufficient to assess state and impact, based on the assumption that complexity will be linked to biodiversity and function (e.g. Crowder & Cooper 1982; Wildish & Kristmanson 1997; Bruno & Bertness 2001; Bolam, Fernandes & Huxham 2002). The structural complexity of a habitat depends on the substrate type and the types of sessile fauna that are present (Auster & Langton 1999). Sessile epifaunal communities, consisting, for example, of soft corals and sponges, on hard mineral substrates or biogenic reefs may create structurally complex environments which modify the environment, increase the area available for settlement and provide shelter for a variety of organisms such as fish recruits and small crustaceans (e.g. Connell & Jones 1991; Beck 1997; Kaiser, Rogers & Ellis 1999; Bradshaw, Collins & Brand 2003).
There have been several attempts to describe habitat complexity in general terms, instead of focusing on species identity and abundance (McCormick 1994 and references therein). These approaches have been adopted to reduce sampling and sample processing costs and to increase the frequency and scale of replication. Most methods for describing complexity have relied on direct measurement, for example, by using a profile gauge or comparing linear distances with distances across a habitat surface (Luckhurst & Luckhurst 1978; McCormick 1994). These measurements, when taken underwater, are usually made by divers and are, therefore, difficult to make over larger spatial scales and are also depth limited. Acoustic methods, such as multibeam sonar (Brown & Blondel 2009), can provide high-resolution descriptions of topographic complexity and substrate type at large spatial scales, but they do not consistently describe the contribution of fauna with soft tissues to habitat complexity.
Here, we explore the performance of two methods for assessing habitat complexity; both are easier to use, faster and cheaper than the manual analysis of biological samples or photographs. The first method is a modern analogue of profile gauge and chain methods and uses a laser line to provide rapid and repeated topographic measurements. The process of the laser line images was inspired by O'Neill, Summerbell & Breen (2009) who showed that a laser line could be used to detect the physical impact of an experimental fishing event. The second method uses seabed photographs to assess two-dimensional heterogeneity within habitats. An index of complexity is calculated from the layout of pixel values in each photograph (Proulx & Parrott 2008). This method has recently been used to describe the complexity of coral reef habitat at different spatial scales (Mellin et al. 2012). Both methods are cost effective as they do not require the collection and identification of fauna. We assessed the capacity of both methods to distinguish between habitats by comparing derived indices of habitat complexity with detailed information on substratum type and sessile epifaunal communities collected at the same spatial scale. To show the ecological relevance of the complexity indices, we tested whether they could be used to quantify ecological patterns such as changes in abundance and richness of associated mobile species.
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- Materials and methods
Our results show how laser lines and the processing of digital images can be used to measure the structural complexity and heterogeneity of a range of seabed habitats. For a given amount of time or financial resources, both methods allow greater levels of replication in space and time than the direct sampling and identification of fauna and substratum types. Both methods can be used to quantify some differences in substratum type, while one of the line laser indices can be used to further quantify the differences in the densities of sessile epifauna.
Clearly, the practical value of methods for describing and monitoring marine habitat complexity depends on whether they meet the operational needs of scientists studying habitat distributions and the effects of pressures as well as the needs of those authorities responsible for habitat mapping, monitoring and assessment. No generic method is likely to meet all needs, but the methods we propose provide information on complexity that can be used to assess state and/or to describe changes in state. Likely, applications would be habitat mapping, monitoring responses of habitat to management measures and the large-scale assessment of human and environmental impacts. Given significant limitations on resources for monitoring and assessment of marine habitat, it is unlikely that detailed species-based analysis would be feasible and/or provide adequate statistical power to detect trends on equivalent space or time-scales. However, confidence in the relationships between habitat complexity and the biodiversity or function of habitat (in particular, those aspects of policy relevance) will likely determine the extent to which information on complexity, as derived with our methods, is deemed sufficient to guide assessment and management.
The dMIG, which was used to quantify heterogeneity on a two-dimensional scale, could differentiate between sand, gravel and larger stones. dMIG, unlike MIG, no longer differentiates between random and ordered images. However, dMIG was appropriate when we were focusing on complexity rather than order and randomness. Surprisingly, MIG appeared to decrease with substrate complexity when an increase was expected. This meant that dMIG increased from sand to gravel and from gravel to cobble and rock. One explanation might lie in the scale and resolution of the photographs. Because we were working at very small scales (0·14 m2 photographs), the presence of larger stones seems to have increased the uniformity (or order) in the picture rather than decreasing it towards more clustered patterns. The MIG index did not capture variations in sessile epifaunal coverage, probably due to similar scale effects.
The LR index provided a quantitative index of complexity that reflected both substratum type and sessile epifaunal abundance; the two components of structural complexity (Auster & Langton 1999). Such a measure of structural complexity has potential value when quantifying relationships between habitats, diversity and ecological processes. Indeed, in this study, the LR index was related to mobile benthic species abundance and richness. The response of abundance and richness to changes in habitat complexity or increases in surface area has generally been difficult to disentangle, because surface area usually increases with surface complexity. One way of dealing with this issue is to study the fractal dimension of the habitats (Johnson et al. 2003). The laser line data allow the calculation of an index of fractal dimensions, as when manual profile gauges or cast cross-sections have been used on rocky shores or mussel beds for instance (e.g. McCormick 1994; Beck 2000; Commito & Rusignuolo 2000; Johnson et al. 2003; Kostylev et al. 2005). However, as no relationship between substratum type or sessile epifauna and fractal dimension was observed here, we could not determine whether abundance and richness responded to complexity or increases in surface area.
The scale of complexity measurements may affect the strength of relationships between complexity, faunal composition and function. For example, Mellin et al. (2012) used the MIG method to look at how hierarchical habitat complexity in coral reefs reflected variations in abundance, richness and community structure in fish populations. They showed that 25% to 33% of the deviance in abundance, richness and community structure could be explained by the MIG index. Similarly, we found that 30% and 25% of the variance in abundance and richness of benthic mobile organisms could be explained by our index of structural complexity, the LR laser line index. Mellin et al. (2012)'s study suggests that a spatially structured design including replicates of laser line measurements and MIG indices from larger scale images could be used to improve the predictive power of our index of complexity in temperate sand, gravel, cobble and rock habitats.
The distribution and status of marine habitats is affected by human and environmental pressures. Bottom trawling is one of the most widespread human pressures (e.g. Eastwood et al. 2007) and can modify habitat structure and associated biodiversity (e.g. Auster & Langton 1999; Thrush & Dayton 2002). Changes in habitat owing to trawling pressure are usually quantified using grabs, dredges and trawls to provide species identity, abundance and/or body size data (e.g. Jennings et al. 2001; Blanchard et al. 2004) or by photographic methods that provide metrics of species' abundance (e.g. Collie, Escanero & Valentine 2000; Lambert et al. 2011). Both approaches are relatively costly and labour intensive, require specialist taxonomic skills and are, therefore, challenging to use for frequent monitoring over large spatial scales. The methods developed in the present study would support fishing impact assessments and monitoring on larger space and time-scales.
As our approach was only semi-automated, it required some operator time to enhance the images. In some images, we also observed a shadow effect where part of the view of the laser line was blocked by the surface. This could largely be solved by using two lasers illuminating the surface from opposite incident angles (see Darboux & Huang 2003 for details). The image processing speed (30–40 min per station of 10–30 images) could readily be increased by developing software to extract the wavelength required at high frequencies so that the number of replicates would be high enough to appropriately represent complex surfaces (Frost et al. 2005). However, the laser line method is limited, just like the profile gauge method, by the incapacity to model features such as overhangs, which may be important as species refuges (Commito & Rusignuolo 2000).
Of the methods considered, the laser line gives more consistent information on habitat complexity. It has potential to support demands for frequent monitoring of habitats and fishing effects on large spatial scales (e.g. monitoring the performance of marine protected areas or the effects of changes in fishery management regulations). It is less costly and labour intensive than other approaches and can be deployed from vessels of many sizes. In comparison, the consultancy price for processing sediment samples, that is, sorting and identification of benthic invertebrates in grab samples, is c. £300 per sample in the UK, noting that several replicates would be required per station to measure habitat complexity based on the biomass of sessile epifauna. The laser model used in this particular study costs c. £150. The housing was not included and was manufactured for the present study but complete models, including housing, are generally more expensive. The sled was about £1500 and the video camera around £8000, including umbilical and lights. The equipment could also be deployed by divers on habitats where the impacts of towing a sled over the seabed were not acceptable, such as coral reefs. Since the 2008 survey, further development and improvement have been made to the method. For instance, we deemed our laser line method suitable although we expected the power of our analyses to be decreased by the lack of direct correspondence between laser line measurements and quantified sessile epifaunal densities (see methods). However, ideally there should be direct correspondence between the two. Therefore, we have now fitted the line laser and a smaller, cheaper, video camera with a wider angle and higher resolution to a beam trawl to relate habitat characteristics directly with catches and by-catches. Work is in progress to redesign the sled with this same camera and assess the impact of fishing on habitat complexity.
Compared with other methods for assessing complexity discussed by Frost et al. (2005): chains, profile gauges and stereo photography, the laser line is likely the most practical, precise and cost-effective instrument for measuring subtidal habitat complexity at large scales. Two of its major advantages are that each measurement can be replicated at high frequencies and with high precision on a particular habitat. Another potential benefit of the laser line method is that the video camera provides a continuous record of the laser line along the length of the transect. Thus, when post-processing, different numbers of ‘samples’ could be taken at random or even fixed intervals along the transect, depending on the objectives of a study. Further, the existence of a continuous record of changes in the laser line might be used to support image processing at very small distance intervals: to describe transitions in habitat complexity and impacts at different scales. While the capacity to describe habitat complexity on habitat at large spatial scales and at high resolution would add to existing monitoring and assessment, it is unlikely that it would entirely replace taxonomic assessment. For studies of human impacts and for habitat monitoring and assessment, we would envisage that the scale and frequency sampling might be increased using a laser line system, potentially combined with the calculation of MIG indices at different scales, but that interpretation would be supported by a lower frequency assessment based on conventional biological sampling or photography.