|Author(s)||Marchal Paul1, Bartelings Heleen2, Bastardie François3, Batsleer Jurgen4, Delaney Alyne5, Girardin Raphael1, Gloaguen Pierre1, Hamon Katell6, Hoefnagel Ellen2, Jouanneau Charlène5, Mahevas Stephanie1, Nielsen Rasmus3, Piwowarczyk Joanna7, Poos Jan-Jaap3, Schulze Torsten6, Rivot Etienne8, Simons Sarah6, Tidd Alex9, Vermard Youen1, Woillez Mathieu1|
|Affiliation(s)||1 : Ifremer, france
2 : DLO-LEI
3 : DTU-aqua, Denmark
4 : DLO-IMARES
5 : IFM-AAU
6 : VTI-SF
7 : IOPAS
8 : AGRO
9 : CEFAS, UK
|Note||Vectors of Change in Oceans and seas Marine Life, Impact on Economic sectors. SP1 - Cooperation Collaborative Project - Large-scale Integrating Project FP7 - OCEAn - 2010 Priject Number : 26645|
|Abstract||The scope of this report is to present the science developed within the VECTORS project to improve the understanding of the key processes driving the behaviour of human agents utilising a variety of EU maritime domains. While particular attention has been paid to the spatial interactions between fishing activities and other human uses (e.g., maritime traffic, offshore wind parks, aggregate extractions), the behaviour of non-fishing sectors of activity has also been considered. Various quantitative and semi-qualitative approaches have been pursued to gain better insight into behavioural drivers based on past data, and also forecast how human agents would react if access was constrained by either management (e.g. Marine Protected Areas – MPA), or the installation of a new operator. This report covers the North Sea and Eastern Channel, and also one area of the Baltic Sea: the Gdansk Bay.
Fine-scale catch and effort data by fishing vessel, fishing trip, gear used and ICES rectangle visited have been made available for the French, Dutch, English and German fleets. The VECTORS WP2.3 team has also collected data from the non-fishing sectors of activity, in particular, aggregate extractions and maritime traffic. The objective of collecting data for non-fishing sectors is to produce a Spatial Overlap Metric measuring the constraint exerted by other sectors of activity on fishing. Comprehensive aggregate extraction and shipping intensity metrics could then be derived dynamically at a fine spatial and temporal resolution. For the other sectors potentially competing for space with fishing (e.g., wind farms, oil/gas extractions, aquaculture), and also for protected areas, a static overlap metric has been set to the surface occupied by the plant or area protected. In order to apply common methodologies and codes across different case studies, whilst abiding by confidentiality issues around these data, it has been decided to develop a common exchange format to collate the data used in subsequent analyses; five tables have then been produced.
Two complementary types of approaches have been carried out to analyse and/or model the mechanisms of human behaviour, which are hereby referred to as quantitative and qualitative research. Quantitative research consisted of analysing fishing decision-making processes based on existing data and then making forecasts building on scenarios, while qualitative research consisted of interviewing stakeholders from different sectors of activity to get their views on both their past and likely future behaviour. Different methodological approaches have been pursued by different institutes, and these were applied to several case studies wherever possible.Modelling the current and past dynamics of fishing vessels:
The understanding of the dynamics of fishing vessels is of great interest to define sustainable fishing strategies and to characterize the spatial distribution of the fishing effort. It is also a prerequisite to anticipate changes in fishermen’s strategy in reaction to management rules, the economic context or the evolution of exploited resources.
In this context, analysis of individual vessel's trajectories offers promising perspectives to describe behaviour during fishing trips. A hidden Markov model with two behavioural states (steaming and fishing) was developed to infer the sequence of non-observed fishing vessel behaviour along the vessels' trajectory based on GPS records. Conditionally to the behaviour, vessels movements were modelled by a discrete time solution of a (continuous time) stochastic differential equation on vectorial speeds. The model's parameters and the sequence of hidden behavioural states were estimated using an Expectation-Maximization algorithm, coupled with the Viterbi algorithm that captures the most credible joint sequence of hidden states. A simulation approach was performed, that outlined the importance of contrast between the model’s parameters as well as the influence of path length to allow good estimation performances. The model was then fitted to four original GPS tracks recorded with a time step of 15 minutes derived from voluntary fishing vessels operating in the Channel within the IFREMER's RECOPESCA project. Results showed differences in parameter estimation depending on the gear used, on both the speeds during fishing operations and the Markovian transitions between behaviours. Results also suggested the benefits of future inclusion of variables such as tidal currents within the ecosystem approach of fisheries.
Hidden Markov models are well suited to describe jointly fishing boat movement and associated fishing activities. They allow us to estimate the sequence of activities (i.e. fishing, travelling) along a trajectory, as well as the movement parameters (speed, turning angle) associated with each activity. Normally, these models are developed to characterize the spatial dynamics of fishing vessels that belong to a specific fishery with a given métier. However, because of the large variability that exists in fishing practices, some adaptations in the modelling structure are needed when the spatial dynamics of one or several fishing fleets present a mixture of métiers with distinct traits of movement and trajectory. A procedure was developed to capture the variability of fishing practices and associated vessel trajectories. Fishing trips were characterized by their métiers, which were identified for each gear by clustering landing profiles (in value). Fishing boat trajectories were described using movement parameters (speed, acceleration, turning angles, straightness) estimated from GPS positions recorded along the tracks. A principal component analysis was performed to provide a detailed description of the different trajectory patterns in relation with fishing trip specificities (i.e. vessel, gear, métier). Hidden Markov models were then fitted for some selected fishing trips. Two types of models were considered. The basic one was a 2 states model with behavioural activities corresponding to fishing and traveling. The second one presented a number of fishing states depending on the number of métiers identified for the trip. Fitting performance was compared based on DIC and estimated confidence intervals for the parameters. This procedure was applied to a set of volunteer vessels participating in the RECOPESCA project from IFREMER in the Bay of Biscay and the English Channel for years 2011-2012. We show that fishing trip activities, such as métiers, were structuring variables for trajectories, which helped to specify properly hidden Markov models.
Discrete choice models building in a random utility function (RUMs) have also been used to aid understanding and modelling of fleet dynamics and to anticipate how fishing effort is re-allocated following any permanent or seasonal closure of fishing grounds, given the competition for space with other active maritime sectors.
A first Random Utility Model (RUM) was developed and initially applied to determine how fishing effort is allocated spatially and temporally by the French demersal mixed fleet fishing in the Eastern English Channel. The spatial resolution of this investigation was that of an ICES rectangle (30’ x 60’). The explanatory variables chosen were past effort i.e. experience or habit, previous catch to represent previous success, % of area occupied by spatial regulation, and by other competing maritime sectors. Results showed that fishers tended to adhere to past annual fishing practices, except for the fleet targeting molluscs which exhibited within year behaviour influenced by seasonality. Furthermore, results indicated generally that maritime traffic may impact negatively on fishing decision. Finally, the model was validated by comparing predicted re-allocation of effort against observed effort, for which there was a close correlation. The method was also applied to the Dutch beam trawl fleet (2008-2010). The Dutch fleets’ activity was well captured by the model which included only biological and economic drivers. Predictions were accurate and followed the seasonal patterns well. To predict the long term changes in fishing activity additional factors, such as the competition for space with other marine users, should be included and changes in fish distribution should be linked to the current model.
A second Random Utility Model (RUM) was developed using a finer spatial resolution (15’ x 15’) and initially applied to analyse the determinants of English and Welsh scallop-dredging fleet behaviour, including competing sectors operating in the eastern English Channel. Results show that aggregate activity, maritime traffic, expected costs, English inshore 6 and French sovereign 12 mile nautical limits negatively impact the choice of fishers, and conversely that past success, expected revenues and fishing within the 12 nautical mile limit have a positive effect on their utility. The model has potential application for Marine Spatial Planning (MSP). This RUM was also used to evaluate the interactions of fishing effort allocation and shipping for the Dutch demersal fleet fishing in the English Channel, this analysis of the French and UK fleets was also undertaken for Dutch seiners operating in the Eastern Channel. The parameters associated with the gross revenue all had positive parameter estimates for the means, as is expected from fishers seeking to maximise net revenues. The parameters associated with costs were also positive, which is striking, given that one would expect a cost minimization. The positive estimates could be caused by the trips to Dutch harbours that are in the data set. The closed area parameters are both negative, reflecting the fact that fishing is not allowed in these areas. The parameters associated with the shipping lanes had negative estimates, as in the French and English case, but these estimates did not differ significantly from zero.
Other spatially-explicit statistical analyses have been conducted to evaluate, separately, the impacts of aggregate extraction and maritime traffic.
In terms of marine aggregate extractions, the effects were investigated of both aggregate extraction intensity and the proximity to dredging sites on the distribution of fishing effort, for a broad selection of French and English demersal fleets operating in the Eastern Channel. The most striking result was that for most of the fishing fleets and aggregate extraction sites, neither dredging intensity, nor the proximity to the extraction site, had a deterring effect on fishing activities. To the contrary, the fishing effort of dredgers and potters could be larger in the vicinity of marine aggregates sites than in their neighbourhood and also positively correlated to extraction intensity with a lag of 0 to 6 months. The fishing effort distribution of French netters was overall space-invariant over the whole time period under investigation. However, it is important to note that the fishing effort of netters has increased substantially in the impacted area of the Dieppe site (where it is correlated to dredging intensity with a lag of 6 months), whilst remaining almost constant in the intermediate and reference areas. The attraction of fishing fleets is likely due to a local and temporary concentration of their main target species. However, knowledge on the vulnerability and life-history characteristics of these species to aggregate extractions suggests that over-extending the licensed areas would be detrimental to them and to their related fisheries in the longer term.
In relation to maritime traffic, we investigated whether fishermen’s effort and catch information could be used to inform on species distributions and if the observed effort (and catches) could be constrained by other activities, such as maritime traffic. In this first attempt to correlate fish distribution observed during a scientific survey and fishing catches via linkage of VMS and logbooks data there was a good correlation between the observed biomass in October by a scientific survey and fishing location targeting the different demersal species in the Eastern Channel. Fleets seem attracted by areas identified to have high abundance densities. For most of the species, maritime traffic seems to be a perturbation for the fishing activities. However, in the case of the red mullet fishery, vessels seem to avoid traffic lanes except when they expect high fish densities. They then may take the risk of fishing inside the traffic lanes or in areas of high marine traffic densities.Eliciting the perspectives of fishers:
To validate the outcomes of fleet dynamics models a survey was undertaken of French and Dutch fishers operating in the Eastern Channel and in the German Bight. In the Eastern Channel, French fishers did not feel constrained in the amount of space they had available for fishing. The one who did, cited Natura2000 and shipping lanes as constraining factors, and said this directly influenced his fishing patterns. Four agreed that they conflicted at times with other fishers (all Dutch purse seiners) over space while one mentioned conflicts with coast guards. One also cited aggregate extraction (when completed) as resulting in no fishing anymore in that area. In the German Bight, Dutch fishers considered that their fishing ground in the German Bight shrank in the past 10 years. They have to fish more intensively now on less available fishing grounds. They have to face sometimes unsafe situations near oil rigs and wind farms. Finally, fishers do not think they have any influence on the increase of competition for space but would like to have more. Closed areas (including real time closures), wind farms and Natura 2000 areas have restricted their activities spatially as well as, to a lesser degree, oil rigs, shipping, and mussel cultures. -German Bight Dutch fishers earn less and catch less targeted species. They have to adapt their fishing pattern. It is crowded now and less safe. Fishers expect more large-area restrictions of a permanent character in the near future in the German Bight but will continue fishing since they feel they have no choice.
Interviews were also conducted with representatives of the main sectors of activity (including fishing but also non-fishing sectors) in the Eastern Channel, the Dogger Bank and the Gdansk Bay. The pressure on spatial usage of European regional seas by various stakeholder groups is intense. In all case study sites, the majority of stakeholders feel this pressure will only increase in the future, primarily due to proposals and plans for offshore wind farms, the newest entrants to these busy seas. In some case study areas, such as the eastern English Channel and Dogger Bank, applications have already been approved with construction planned; in the German Bight wind farms already exist and at least in the Dutch part new proposals have been made . In others, such as the Gulf of Gdansk, such developments are further away, and unlikely to happen soon due to legal constraints, yet the uncertainty of impacts, such as on fishing, is a cause of great concern. Of all the stakeholder groups, the fisheries group was the only one addressed by all surveys and thus can be compared across all case study areas: eastern English Channel, the Dogger Bank, the German Bight, and the Gulf of Gdansk. Fishing is one of the oldest activities in all four of the case study areas and though fishers in each area tend to use different gears and face different pressures, there are a number of similarities among them. These pressures include: regulatory pressures, competition with other users, and area restrictions.Modelling the future dynamics of fishing vessels:
Having analysed the key determinants of fishers’ and other stakeholders’ decision-making behaviour, the future effects of increased resource-based competition (resulting from a discard ban combined with restrictive individual fish quotas) or of new area-based constraints (e.g., implementation of new wind farms or enforcement of new legally-binding closed areas) were evaluated in the short- and/or the long-term using bio-economic models.
The short-term economic effect of implementing wind farms in combination with closed areas was studied in the North Sea using an Individual Stress Level Analysis (ISLA). In this study we used the spatial data of the Vessel Monitoring System (VMS) coupled with logbooks to assess the stress potentially caused by closure of fishing areas. The stress was expressed in financial terms as the percentage of current revenue obtained from catch coming from areas to be closed. It is therefore not a loss but more a measure of the level of reallocation of fishing needed to maintain the current revenue.
We used the A2 (“National enterprise” scenario) and B1 (“Global community scenario”) VECTORS scenarios to design potential closures due to wind farms, nature conservation areas and maritime traffic in the North Sea and we calculated the stress caused by the closures on the Dutch and German 2010 fishing fleets. The scenarios investigated envisage large closures leading to stress levels of 7 to 15% for the Dutch fleet and 3 to 5% for the German fleet. Almost all of the Dutch vessels would be impacted by the closures (more than 90% of vessels in both cases) while the German fleet would be slightly less impacted (around 55% of vessels impacted for A2 and 65% for B1). All Dutch harbours would have seriously impacted vessels (>15% of revenue) for both scenarios, although the proportion of those vessels would be higher in B1 scenario, especially in the southern harbours. The German harbours would be less impacted with only Büssum showing hosting impacted vessels.
We then examined the longer-term effects of fishers competing for resources and/or space. First, we explored the potential effects of a discard ban in mixed fisheries management using the French mixed fisheries in the Eastern English Channel as a model system (DSVM IBM model). The model evaluates a time series of decisions taken by fishers to maximize profits within management constraints. Compliance to management was tested by applying a tax for exceeding the quota, which was varied in the study. We then evaluated the consequences of individual cod quota in both scenarios, with respect to over-quota discarding, spatial and temporal effort allocation and switching between métiers. Individual quota management without a discard ban hardly influenced fishers’ behaviour, as they could fully utilize cod quota and continue fishing other species while discarding cod. In contrast, a discard ban forced fishers to reallocate effort to areas and weeks where cod catch is low, at the expense of lower revenue. In general, a restrictive policy for individual quota for cod needs to be combined with a discard ban and a high tax to reduce over-quota discarding.
Second, we evaluated the long-term bio-economic effects of a closed area, using the FISHRENT model, for a variety of scenarios. Regulations and changes in market and environmental conditions may change the profitability of one fishery and can lead to reallocation of fishing effort. The extent of this effort displacement will depend on the relative profitability of the alternative options for the fleet segments affected. When fishing areas and fleet segments are heterogeneous, simple aggregate effort models such as those based on the ideal free distribution theory may provide inaccurate predictions. A bio-economic optimization and simulation model was applied to explore how the different conditions of A2 (“National enterprise” scenario) and B1 (“Global community scenario”) could impact the fishing effort allocation and the distribution of benefits across fleet segments from different nations. In the model the optimization of net profits determines the effort adjustment and the investment behaviour of fleet segments, which in
Marchal Paul, Bartelings Heleen, Bastardie François, Batsleer Jurgen, Delaney Alyne, Girardin Raphael, Gloaguen Pierre, Hamon Katell, Hoefnagel Ellen, Jouanneau Charlène, Mahevas Stephanie, Nielsen Rasmus, Piwowarczyk Joanna, Poos Jan-Jaap, Schulze Torsten, Rivot Etienne, Simons Sarah, Tidd Alex, Vermard Youen, Woillez Mathieu (2014). Mechanisms of change in human behaviour. WP2.3 D.2.3.1. https://archimer.ifremer.fr/doc/00223/33377/