Andre presfaktorer end næringsstoffer og klimaforandringer – effekter af fiskeri på de marine kvalitetselementer bundfauna og fytoplankton

Research output: Book/ReportReport

Abstract

As part of a larger project funded by the Danish Environmental Protection Agency (“Effects on the quality elements defined by the EU Water Framework Directive (WFD) of other pressure factors than excess nutrient load and climate change”), fishery with trawl, dredge or other mobile bottom-contacting gear (MBCG) has been identified as a potential risk to all of the quality elements of the WFD (Petersen et al. 2018). In this report, we have analysed effects of fisheries on i) benthic fauna using statistical methods to correlate data on fishing pressure with data on fauna composition and ii) phytoplankton using a 3D dynamic model to show effects of mussel fishery on Chlorophyll a concentration (Chl.a) and iii) cascade effects on phytoplankton and nutrient concentrations in a review study of the literature.

For impact on benthic fauna, we have mapped fishing pressure in Danish water bodies using information of vessel activity from VMS (Vessel Monitoring System), AIS (Automatic Identification System) and the BB (Black Box) system. The BB system is used only in the mussel and oyster fishery and monitors vessel position and activity every 10th second, allowing for fishing pressure to be mapped very precisely. The AIS and VMS systems are much less precise as they only record vessel activity once an hour (VMS) or at irregular intervals and vessel coverage (AIS). Slightly less than 50% of the 119 Danish water bodies had fishery with MBCGs in the period 2014-18. Based on data from all three monitoring systems, a map of fishing intensities (Swept Area Ratios [SARs]) in quadrats of 100 x 100 m was produced for all water bodies. In terms of spatial extent (proportion of water body seabed area impacted) fishing pressure was highest along the west coast of Jutland, in Ålbæk Bight, and south of Fyn (Figure 2.1). In the same areas, fishing intensity was also highest, with substantial parts of the seabed in the water bodies being impacted between 1 and 5 times a year, but also in several other water bodies local hot spots had substantial areas with similar levels of intensity.

Due to the lack of precision in VMS and AIS monitoring data (i.e. the associated uncertainty in the exact location of the fishing pressure), only BB data was used for an analysis of effects of fishery on benthic fauna. Data collected in the Danish NOVANA program (2014-18) was used as input data on benthic fauna composition. The combined BB and NOVANA data set consists of 30 stations in 16 water bodies or a total number of samples of 1669. The DKI (Danish Quality Index) is used in the Danish WFD management as indicator for benthic fauna status, and for all 1669 samples a DKI value was calculated according to Henriksen et al. (2014). DKI values together with associated information on water depth and information on oxygen content (where available) was matched with estimates of fishing intensity (SAR) at each sampling location, and subsequently analyzed with General Linear Mixed Models, with station and year as random effects. Surprisingly no significant effect of fisheries on DKI could be detected, only depth, abundance of species and abundance of individuals had a significant effect on DKI values (Figure 2.4, Table 2.2). We suggest that the lack of effect of fisheries on DKI in the analysis may be a result of: i) that DKI is designed specifically to detect effects of eutrophication, not fishery, and thus gives high weight to species number; and ii) that the effects of fishery may be masked (and comparatively small) in areas already heavily disturbed by eutrophication.

As mussels and other bivalves feed on phytoplankton, fishing on bivalve populations may reduce benthic filtration pressure and lead to an increase in Chl.a. A local 3D Flexem model (Larsen et al. 2017) was established for a 1,50 x 1,05 km blue mussel bed in Løgstør Bredning, Limfjorden. The model was forced with current velocities generated by a Limfjorden scale model run for the period May 1st to December 31st 2017 and vertical mixing was created by wind speeds generated by a meteorological model. T, S and Chl.a was generated at the open boundaries of the model using data from the NOVANA monitoring program. A mussel bed was placed in the middle of the model area with a size of 250 x 100 m (50 cells of 50 x 10 m), which corresponds to the observed spatial distribution of mussel banks in the Limfjorden and with an abundance corresponding to natural conditions (895 ± 419 ind. m-2, Figure 3.1). Mussel filtration and growth per individual were based on a dynamic-energy-budget (DEB) model (Maar et al. 2015).

The model was run as 2 series (A and B) with a total of five different setups. In the model references there is no fishing on the mussel bed. The references were divided into A and B for respectively measured Chl.a and halved Chl.a concentration (Table 3.2). In the fisheries scenarios, fishing reduced total mussel biomass with 8 or 16% corresponding to standard fishing effort in the Limfjorden. Model results showed that there in general was an increased Chl.a concentration with increased fishing and this effect was visible over time both in the middle and at the outer edge of the mussel bed (Figure 3.2B-C). The average maximum Chl.a increase across the mussel bed corresponded to 2-4% (May to December) in the two A scenarios, while in scenario B it was <1% of the background concentration. The changes were thus relatively small. On a smaller scale, within the mussel bed itself, short-term major changes may occur (Figure 3.2C). Increased fishing from e.g. several boats in the same area will give a correspondingly greater negative effect. The overall effect in an area will depend on the spatial distribution and composition of the mussel beds, their mutual competition for phytoplankton.

Overall, we estimate that the stated estimates of the potential importance of mussel fishery for phytoplankton biomass expressed as Chl.a concentration are within a realistic range and that estimates will not change much even if several factors and parameters are included in the model.

Studies from lakes have shown that fishery on selected species (biomanipulation) can lead to cascade defects in the food chain and ultimately lead to better water quality. However, there are very few studies of cascade effects due to changes in fisheries in marine ecosystems, as it is difficult to detect in open ocean systems with more complex food web structures and the influence of water exchange with other areas, species migration and other pressure factors. Here we have conducted a review of the relevant literature from marine conditions. Based on the published studies - both statistical analyzes and models of variable complexity - it is likely that overfishing and dramatic changes in fish stocks can cause cascade effects in the marine food network, even in inner Danish waters. However, a relatively large change in fisheries practice is required before the cascade effect is visible in phytoplankton biomass and nutrient concentrations. Thus, as long as fisheries are managed sustainably (i.e. not overfishing), it is likely that minor fluctuations in fish stocks will not cause major changes in phytoplankton or nutrient concentrations.
Original languageEnglish
Place of PublicationKgl. Lyngby
PublisherDTU Aqua
Number of pages46
ISBN (Electronic)978-87-7481-280-7
Publication statusPublished - 2020
SeriesDTU Aqua-rapport
Number358-2020
ISSN1395-8216

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