Linking fish and prawns to their environment: a hierarchical landscape approach

Abstract

Little is known about the relative influence of landscape structure on the spatial distribution and abundance of marine organisms. To address this problem, we applied landscape ecology concepts and methods, together with conventional sampling techniques and path analysis, to test alternative hypotheses of linkages between marine nekton and landscape structure in Moreton Bay, Queensland (Australia). We quantified substratum structure at 3 spatial scales: (1) whole landscape mosaic (10s of hectares); (2) habitat type (benthic class) (100s m 2 to hectares) and (3) within-patch scale (cm 2 to m 2 ). Substratum structure at all scales was important for assemblage density and number of species, with the landscape structure of individual habitat types explaining more of the spatial variation than either within-patch structure or the structure of the whole landscape mosaic. Density and the number of species of seagrass residents were strongly influenced by landscape composition quantified as the proportion of all seagrass habitat (r 2 = 0.40 and 0.48 respectively) and the proportion of long-leaved Zostera capricorni (r 2 = 0.34 and 0.30 respectively) seagrass in the landscape. An abrupt decline in assemblage density and number of species was evident at < 20% seagrass cover. More species of fish used mangroves with adjacent continuous seagrass beds than mangroves with adjacent patchy seagrasses or unvegetated sandflats. Several species of fish using mangroves at high tide were more strongly influenced by the spatial configuration of mangrove patches and the composition of adjacent substratum than the internal structure of mangrove patches. The study highlights the need for a hierarchical landscape approach when investigating animal-environment relations in marine landscapes.

Panarchy: Discontinuities Reveal Similarities in the Dynamic System Structure of Ecological and Social Systems

“…A pattern of peaks and troughs persisted regardless of the temporal and spatial variation in the data set (Stead et al 2005). The three-dimensional structure of habitat provides a strong predictor of the body masses of the species dependent upon that habitat (Gutierrez and Iribarne 2004), and others continue to document a relationship between discontinuities in body size distributions and habitat structure in freshwater fish (Fu et al 2004) and nekton (Pittman et al 2004). Robson et al (2005) argued that studies must once again emphasize habitat structure and body mass to refine the methodology and synthesize results from pattern seeking and mechanistic research.…”

Abiotic and biotic factors influence the habitat use of an estuarine fish

“…Previous authors have noted that there is no single correct scale at which to quantify the spatial distribution of populations and have suggested that habitat use must be examined on multiple scales (Wiens 1989, Levin 1992. Recently, the importance of scale in the interpretation of spatial distribution of aquatic organisms has been noted (Essington & Kitchell 1999, Maury et al 2001, Pittman et al 2004). Essington & Kitchell (1999) showed telemetered largemouth bass distributions in a small Michigan lake were the product of several processes operating at spatial scales of 10, 30, and 180 m. The authors concluded that the small-scale aggregation may have been a response to patches of aquatic macrophytes, while large-scale variation was a response to selection of the eastern half of the lake, possibly due to warmer water temperatures.…”

Mapping Fish Community Variables by Integrating Field and Satellite Data, Object-Based Image Analysis and Modeling in a Traditional Fijian Fisheries Management Area

“…Habitat richness was also of low importance, regardless of model type and spatial scale. Although this corresponds to findings from other studies (e.g., [16,17]), the question remains whether other measures of the spatial composition of habitats, e.g., taking into account functional differences or complementarities between habitat types [63], would contribute more to predictions of the fish community variables. The success of landscape composition metrics in terrestrial spatial ecology [64] suggests that this is an important avenue for future research.…”

“…In the random forest models, the high importance of geomorphic zone for predictions of all response variables is interesting because this predictor has been investigated by very few studies [13], including several that have explored the use of a large variety of metrics for quantifying the seascape (e.g., [16][17][18]32,61]). The importance of geomorphic zone in our study may be a function of the spatial extent of our study area, a function of the high accuracy with which this variable was mapped, a function of its collinearity with another predictor, and/or a true description of the importance of reef geomorphology for structuring the spatial distribution of the three response variables in our study area.…”

“…For example, the positive effect of structural complexity on reef fish species richness and abundance has been demonstrated experimentally [7], and areas of high structural complexity have been shown to contain more species [8] and greater total biomass [9] compared to areas with less structure. Other factors have shown similar correlations with one or more aspects of reef fish communities, such as water depth [10], disturbance history [11], coral cover [12], location on the reef [9] and shelf [13], presence of seagrass beds and mangroves [14,15], as well as benthic composition and patch structure [16][17][18]. Such environmental variables, and the patch mosaic they create, interact with and influence the local fish communities at a range of spatial scales.…”