Aquatic Environment and Biodiversity Annual Review 2012

AEBAR 2012: Benthic impacts
megafaunal production, dominated by scallops and urchins. Hiddink et al. (2006) estimated that more
than half of the southern North Sea was trawled sufficiently frequently to depress benthic biomass by
10% or more, and that 27% was in a state where benthic production was depressed by 10% or more.
They estimated that recovery from this situation would take 2.5–6 years or more once fishing effort
had been eliminated. They further estimated that fishing reduced benthic biomass and production by
56% and 21%, respectively, compared with an unfished situation. Reiss et al. (2009) found that,
although sediment composition was the most important driver of benthic community structure in their
North Sea study area, the intensity of fishing effort was also important and reductions in the
secondary production of the infaunal community could be detected even within this heavily fished
region.
The types of models developed by Hiddink et al. (2006, 2011, but see also Ellis and Pantus 2001 and
Dichmont et al.(2008) can be used to assess the likely performance of different management
approaches or levels of fishing intensity. Such management-strategy-evaluation (MSE) methods
involve specifying management objectives, performance measures, a suite of alternative management
strategies, and evaluating these alternatives using simulation (Sainsbury et al. 2000). For instance, the
early study by Ellis and Pantus (2001) assessed the effect of trawling on marine benthic communities
by combining an implementation of the spatial and temporal behaviour of the local fishing fleet with
realistic ranges for the removal and recovery of benthic organisms. The model was used to compare
the outcomes of two radically different management approaches, spatial closures and reductions in
fishing effort. Lundquist et al. (2007, 2010) used a more sophisticated spatially explicit landscape
mosaic model with variable connectivity between patches to assess the implications of different
spatial and temporal patterns of disturbance in the model landscape. They found that the scale of the
disturbance regime (which could be trawling or any other physical disturbance) and the dispersal
processes interact, and that the scales of these processes greatly influenced changes in the structure
and diversity of the model community, and that recovery across the mosaic depended strongly on
dispersal. System stability also decreased as dispersal distance decreased.
7.3. State of knowledge in New Zealand
To understand the effects of mobile bottom fishing methods on benthic habitats, it is necessary to
have knowledge on
• the distribution of such habitats,
• the extent to which mobile bottom fishing methods are used in each habitat (the overlap),
• the consequences of any such disturbance (potentially in conjunction with other disturbances
or stressors), and
• the nature and speed of recovery from the disturbance.
These components will be dealt with in turn.
7.3.1. Distribution of Habitats
Mapping of benthic habitats at the large scales inherent in fisheries management is expensive and
time-consuming so the New Zealand government commissioned an environmental classification to
provide a spatial framework that subdivided the TS and EEZ into areas having similar environmental
and biological character. This Marine Environment Classification (MEC) was launched in 2005
(Snelder et al. 2004, 2005, 2006) using available physical and chemical predictors, and because
environmental pattern was thought a reasonable surrogate for biological pattern. The authors
suggested that the MEC provided managers with a useful spatial framework for broad scale
management, but cautioned that the full utility and limitations would become clear only as the MEC
was applied to real issues. They described the MEC as a tool to organise data, analyses and ideas, and
as only one component of the information that would be employed in any analysis. The 20-class
version (Figure 7.5, Table 7.1) has been the most widely cited, although additional classification
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