Seals / focas / phoques

Admin Nombre de messages : 25268 Date d'inscription : 17/05/2007

The main prey species of the target fish species should be identified, and estimates of consumption of each prey species made. This is especially important when the target species prey upon their own species or each other, or on species which are also preyed upon by the marine mammals. In some cases it may be necessary to include one or more additional prey species which are of particular interest to managers, even though they would not normally be included on the basis of their consumption by the marine mammal in question.
2.3.3. Other components of the ecosystem
Conceivably, other species in the system, besides those already discussed in Sections 2.3.1 and 2.3.2, could play a significant role in the response to a cull. For instance, if other predators of the target species are significant, then changes in the abundance of those other predators might also be significant and the model would need to predict such changes. This might require detailed information about the predators and the prey of those other predators of the target species. By the same reasoning, one might need detailed information about the predators and prey of the predators and prey of those other predators of the target species, and so on: Indeed, one might need to know all the interactions in the whole system. However, full information of this kind (say, accounting for at least 80% of consumption of all species in the system) will rarely, if ever, be available. Therefore, the SAC agreed that such multispecies issues could be addressed in the following way.

Admin Nombre de messages : 25268 Date d'inscription : 17/05/2007

All the major taxonomic groups in the ecosystem (i.e. species feeding, or being preyed upon, in the area of interest, for part or all of the year) should be identified, as well as the trophic interactions between them (who eats whom). Initially, this information should be assembled in qualitative terms: i.e. include all significant components and trophic interactions, without quantifying them. The information is best expressed as a two-way table, with rows for prey and columns for predators. When this has been done, available data should be searched for a range of estimates of abundance, in biomass, of as many components as possible. Estimates of the total food consumption, and the consumption of individual prey species, should be entered where available. Gaps in the table should then be filled with reasonable guesses, in such a way that the "books balance". The distinction between estimated and guessed values should be kept clear. For estimated values the source of data should be listed, while for guessed values a brief explanation of the rationale should be provided.
Estimation of food requirements based on general allometric relationships relating energy requirements to body size for different groups of organisms should be used to derive a plausible range of estimates where specific data are lacking.
This table will be used for deciding what species should provisionally be taken into account in an evaluation of the likely effects of the cull.
Taxonomically similar species should not be lumped together if there are substantial differences in their distribution and diet, such as between species occurring inshore and offshore.

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Information on the fisheries
2.4.1. Catches
For each fishery which exploits the target fish species, reliable data should be provided on total quantities of fish caught (including those which are discarded), their size distribution and the location and season of the catches. Some fisheries may need to be included on the basis of their bycatches alone if these are significant.
Data are also required on the catches of other species, including both commercially valuable species and bycatches, by each of these fisheries.
2.4.2. Management
In most instances, a cull of marine mammals would be intended to increase the abundance of fish stocks, on the assumption that this would lead to greater catches and profits for the fishing industry. However, the extent to which potential increases in a fish stock are reflected in increased catches can depend on the way the fishery is managed.
In order to evaluate the possible effects of a cull on a fishery, it is necessary to specify the way in which the fishery is to be managed during and following the cull. Typically this is accomplished by setting management measures, such as effort controls or a Total Allowable Catch (TAC), on an annual basis, using an assessment of the state of the stocks at that time, which is in turn based on a series of catch and other data collected up to that point. The method by which the annual TAC is determined or calculated should be specified, so that this can be included in simulations of the effect of a cull under various scenarios.
If there is no clearly defined procedure for the determination of management measures (e.g. TACs), the main objectives of the fishery management regime should be specified.

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Economics
A full evaluation of the economic effects of a cull is beyond the scope of this protocol. Nevertheless, the economic objectives of the cull, if any, should be stated, since these help to guide the choice of biological and technical indices (such as the predicted level of catch for a given period into the future, or the level of fishing effort required to achieve a given catch) used to summarize the predicted effects of a cull, and to ensure that they are relevant.
A full economic analysis of the effects of a cull on a fishery would require information on operating costs, capitalization, market conditions, subsidies, taxation, etc. However, even in the absence of such an analysis, certain basic economic information should be provided where appropriate. For example, if the main fishery of interest catches several species of commercial value, data on the relative prices of each should be given, so that predictions of the level of catch by species can be converted to a total catch value. This may be particularly relevant in cases where the ecological modelling analyses suggest that the marine mammal cull may increase the abundance of some commercial fish species but reduce the abundance of others.
It should be emphasized that increases in the potential yield of a fish stock do not necessarily translate into an increase in the net economic yield of the fishery. In the case of fisheries which are inadequately managed and dependent on high levels of subsidies, enhancing the potential yield of the fish stock, through a marine mammal cull or other means, can even exacerbate the net economic losses made by the fishery. However, these issues are beyond the scope of this protocol.

Admin Nombre de messages : 25268 Date d'inscription : 17/05/2007

Details of the proposed cull or culling programme
There are several types of control programmes which can be used to reduce marine mammal populations, such as culls and bounties maintained for several years or events limited to one or two years. The effects of the control programme will depend upon its extent and the methods chosen.
The number of marine mammals to be killed each year should be specified, along with details of how this number has been determined, the predicted age and sex of the animals, where and when they would be taken and how long the culling programme is expected to continue. The proposal should also note the precise timing in relation to the annual cycle of the marine mammal population, such as a cull during or immediately after the reproductive period or an opportunistic hunt throughout the year. If the number to be killed is not fixed in advance, but is dependent on data to be collected, such as a fixed proportion of the estimated pup production in each year, then the formula for determining the number and class of animals to be culled should be specified, along with details of the data which will be used to implement the formula.
If the number to be culled is not fixed or based on a fixed formula, for example if it is based on a bounty programme to provide an incentive to people to kill the marine mammals opportunistically, then the nature of the incentives should be specified, along with a rough assessment of its likely effect in terms of expected numbers killed, plus any relevant data from past experience.
The intended effects of the cull on the marine mammal population, such as to reduce it to a specific target level, should be specified.
Details should also be given of how the cull is to be implemented and what measures are proposed to verify or estimate the numbers of animals which are actually killed, as well as the scientific information to be obtained from the animals killed and its intended use.

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The intended procedures for monitoring the marine mammal population (such as annual surveys of abundance) during and after the cull period should be specified.
2.6. Provisions for monitoring the effects of the cull
The provisions, if any, for monitoring the effects of the cull, with a view to ascertaining whether it had the intended effect, should be specified. Specific monitoring provisions should be undertaken with respect to each of the major elements in the objectives of the cull and the expected effects of the cull on the fishery and the marine mammal population. Management decisions to continue, stop or modify a cull may be linked to the results of the monitoring programme.
3. EVALUATION OF CULL PROPOSALS
3.1. Introduction
This section describes the procedure for evaluating, in advance, the likely effects of a proposed cull, especially in terms of the likelihood of achieving its objectives. The specified evaluation should be completed before a cull is undertaken.
Although the focus of this protocol is on the prior evaluation of a proposed cull, the approach can in principle also be used to evaluate the effects of a cull that has already taken place. However, delaying the evaluation until after the cull has taken place is not recommended for two reasons: (i) the prior analysis might indicate that the cull has a high probability of being detrimental to the stated objectives, and hence it would disadvantageous for the cull to take place; (ii) the prior analysis might reveal that in order to enhance the reliability of the assessment of the likely effects of the cull, certain data need to be collected from the pre-cull system. Without these data, it may be considerably harder to determine whether the cull had the intended effect.

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The prior evaluation should also include an evaluation of the chances that the planned means of monitoring the effects of the cull will yield reliable results.
The recommended method of evaluation involves the ecological modelling exercise detailed in the following sections. The modelling approach used - scenario modelling - has been shown to be an useful and versatile tool in addressing fishery management questions. The exercise will not necessarily yield a definite verdict as to whether the proposed cull will be beneficial or detrimental, but it may yield an indication of the relative likelihood or plausibility of the cull having positive or negative effects with respect to its objectives. The evaluation exercise may also pinpoint those key aspects of the system on which more information would enable the effect of the cull to be predicted and monitored with more confidence.
It is recognized that in some cases the available modelling expertise may not be available, or there may be features of the situation that make the modelling exercise described below unduly difficult, in which case the evaluation may be performed in a less formal way. In such cases, it is nevertheless recommended that the following sections be studied and that: (i) the data that would be required to implement a model of the type described are assembled; (ii) all the major features which are recommended for inclusion in the modelling exercise should be assessed for their relevance and taken into account in the evaluation to the extent possible.
Table 3 lists the main steps involved in the evaluation process.

Admin Nombre de messages : 25268 Date d'inscription : 17/05/2007

Procedure for the evaluation of the cull proposal

Verify that data listed in Table 2 are complete and adequate
Identify species, fisheries and other components to be included in a minimal realistic model of the system
Specify quantitative performance measures relevant to cull objectives
Construct simulation model of the system
Specify a range of alternative scenarios, covering the range of plausible parameter values and model structures
Run multiple replicate simulations of each scenario and compute performance measures and other relevant statistics
Interpret and synthesise results
Evaluate power of indices proposed to monitor effects of cull

.
3.2. Structure of the evaluation
The evaluation exercise involves the following steps:
(1) Summarizing the available information, including a determination of whether the information specified above is complete and of sufficient quality to conduct an evaluation of the possible effects of the cull. In particular, the information needs to be sufficient to construct a reasonable simulation model of the system, as described below.
(2) Identification of those components of the system which need to be taken into account for at least a minimal realistic evaluation of the likely effects of the proposed cull: these will include the marine mammal population, the cull itself, the target fish stock or stocks, other major predators of the target fish stocks, and, where appropriate the fishery, the management of the fishery, and other relevant components of the ecosystem.
(3) Construction of a simulation model of the system which includes as submodels each of the components identified under (2) and all significant interactions between these components.

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Specification of quantitative performance measures and other model outputs which are used to summarize the predicted performance of the cull relative to its objectives, and to throw light on other relevant aspects of the predicted effects of the cull. For example, if one of the objectives is to increase catches from a given fishery, then the predicted average annual catch by this fishery over the next 10 or 20 years would be a relevant performance measure. Other relevant output statistics include the extent to which the marine mammal population is predicted to be reduced by the cull.
(5) In general the available information will be insufficient to specify uniquely the most appropriate choice of models and parameter values. Hence, a variety of different scenarios should be constructed to span the range of plausible alternatives with regard to model assumptions and parameter values.
(6) In most cases some important components of the system, such as annual recruitment to the fish stock, will be subject to unpredictable random variability. The model should explicitly include this random variability, and multiple replicate simulations (typically 100 or more) of each scenario should be run so as to indicate the range of probable outcomes.
(7) Each replicate of each scenario should be run with and without the proposed cull, so that the differential effect of the cull on the predicted outcome can be analysed. The performance measures and other relevant output statistics should be summarized in a form to assist with understanding of the results, and, if possible, to reach an overall verdict on the relative merits or demerits of the proposed cull, or, failing that, to pinpoint those aspects of the system where more data are needed to reach a conclusion.

Admin Nombre de messages : 25268 Date d'inscription : 17/05/2007

Some of these steps are elaborated in more detail in the following sections.
3.3. Construction of the simulation model
A simulation model should be constructed which contains the following submodels to simulate the components of the system:
3.3.1. Population dynamics of the marine mammal
This submodel should normally simulate the marine mammal population by age and sex over time, and be constructed so as to make use of available information on population parameters of the marine mammal (reproduction and mortality, age at maturation, weight at age by sex, etc). The submodel should contain some density dependence, to avoid the unrealistic prediction that the population will expand indefinitely in the absence of a cull. Since density dependence is hard to measure in populations directly, it may be necessary to make a range of assumptions about density-dependent processes, such as a range of values for the carrying capacity of the environment. For mammals dependent on a highly variable environment, whose populations are liable to fluctuate even in the absence of culling, at least some of the simulated scenarios should reflect this variability; an example would be the effects of El Niño on certain pinnipeds.
If the diet of the marine mammal is sufficiently varied (within or between years), or if the fish stocks of interest constitute only a minor portion of the marine mammal’s annual diet, it is probably not necessary to model explicitly the dependence of the marine mammal on its food resources. Otherwise, a range of assumptions may have to be made relating the population carrying capacity to the available food.

Admin Nombre de messages : 25268 Date d'inscription : 17/05/2007

The proposed cull
The submodel should allow the cull to be simulated by removing given numbers of marine mammals by age and sex from the simulated population.
The proposed cull may not always specify a fixed number of animals to be taken, but may involve a variable number to be taken over time depending on estimates of the remaining numbers of animals. In such cases, the formula needs to be specified for determining the number of animals to be culled, the type of data on which this calculation will be based, and the frequency and precision with which it will be collected (for example, annual pup counts with a given coefficient of variation; see also Section 3.3.7 - Data collection submodels).
3.3.3. Food consumption of the marine mammal
This submodel should allow data on the diet composition of the marine mammal, preferably broken down by population component, such as mature and immature males and females, to be used to predict the total consumption of the species of interest by the simulated mammal population as a function of the simulated numbers of mammals by age and sex at any time in the future.
For this purpose it will be necessary to make assumptions about how diet composition might vary as the availability of the various food items changes. It will not usually be realistic to assume that the diet composition is independent of the relative abundance of food items, although this assumption might be used to construct one extreme scenario to bracket one end of the range of possibilities. A suitable intermediate assumption is that the relative proportions of the different food items vary in proportion to the ambient abundance of the respective items, i.e. relative food preferences do not vary. If the food species under explicit consideration constitute only a minor part of the total diet of the mammal, this assumption can be approximated by the assumption that the mammal exerts a fixed mortality rate on the species of interest.
An alternative scenario to include as a sensitivity test could be that the mammals focus on the more abundant food items, provided that this is consistent with the available data.

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Dynamics of the target fish stock or stocks
In the case of fish stocks subject to an existing programme of assessment and management, it is preferable that the submodel for the dynamics of the target fish stock or stocks is equivalent to that used for the routine assessment and management of the fishery. However, if the model used for the routine assessment of the fishery is purely a stock reconstruction model, without predictive power, it will be necessary to extend it accordingly.
Given the variety of fishery assessment models in current use, and the need to match the choice of model to the specific characteristics of the fish stocks or fisheries in question, it is not appropriate to prescribe the details of the model here. However, the model does need to be of a form to enable predation on the stock by the marine mammals and other predators to be modelled explicitly. Since different predators, including fishers, often focus on different sizes of fish, it will normally be necessary to use a size- or age-structured model. Prediction of the future trends in abundance of the fish stock requires assumptions about recruitment to the stock, in the form of a stock-recruitment relationship, or the assumption of stock-independent recruitment levels. Since recruitment to most fish stocks is subject to considerable unpredictable variability, the assumed recruitment function should include an appropriate level of random variation. This will necessitate simulating multiple replicates of each scenario, whose outcomes will differ according to the different random patterns of annual recruitment that occur in each.

Admin Nombre de messages : 25268 Date d'inscription : 17/05/2007

Other species
If an attempt to predict the outcome of a cull were to consider only the commercial fish species and the marine mammal thought to be one of its important predators, and ignored all other species in the ecosystem, then it is almost inevitable that the analysis would conclude that a cull would lead to increases in the size of the fish stock. However, the limited experience to date in examining interactions between marine mammals and commercial fisheries suggests that restricting the scope of the analysis to only these components of the system is liable to yield misleading predictions of the effects of a cull.
It is desirable to include as many participants in the interaction as possible in models which are used to investigate the consequences of culls. However, it is recognized that increasing the number of species in the simulation model to make it more realistic also rapidly increases its complexity and decreases the ease and confidence with which the results can be interpreted. There is a trade-off to be made between these two tendencies, which makes it difficult to give a firm guideline for the minimum adequate number of components to be included in the system.
The question of whether reliable predictions can be obtained by singling out a few key species and ignoring the others is still the subject of ongoing research from which general conclusions have not yet emerged. Nevertheless, a model which contains so few components that only one kind of outcome is possible regardless of the input data, is clearly insufficient.
At a minimum it is recommended that the models include enough components so that: (i) more than one type of behaviour is possible, depending on the input data; and (ii) the species included account for most (e.g. typically 80%+) of the predation mortality (including cannibalism, where this occurs) experienced by each age class of the commercial fish species of concern (data for the earliest life stages may not be available).
Judgement also needs to be exercised as to which interactions between the species in the model need to be included. To keep the model simple, it can be assumed that if the species in the model make up only a small part of the total diet of another species in the model, then the dynamics of the latter will be independent of the species modelled. For example, if the fish species included in the model make up only a small part of the diet of the marine mammal, then the predation impact of the marine mammal on the fish needs to be included; however, the impact of prey abundance on the population dynamics of the marine mammal need not be considered explicitly.

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In the early stages of model formulation, information on other species (Section 2.3.3 - Other components of the ecosystem) can be incorporated selectively in order to achieve a balance between workability and completeness of the model. Models with differing levels of inclusiveness of other species in the system may be a part of the variety of different modelling scenarios considered (Section 3.2 - Structure of the evaluation). However, care should be taken not to permit too great a difference between models in the quality of data utilized. In many cases this requirement will limit the extent to which information about other species can be included.
3.3.6. The fishery and its management
The extent to which it is appropriate or feasible to model the fishery and its management will depend on the objectives of the cull and the nature of the fishery and the arrangements for its management, if any. Three main types of situation can occur:
(i) In the case of fisheries not subject to quantitative management restrictions, the aim of the proposed cull may be to enhance the abundance of fish, and thereby provide increased catches to the fishery at the existing level of fishing effort. In this case, it may be sufficient to model the relationship between the abundance of the fish stock and the level of catch for one or more given levels of effort, on the assumption that the cull will have no effect on the level of fishing effort.
(ii) In the case of fisheries subject to quantitative management restrictions, such as TACs, these will usually be based on some form of assessment of the fish stock. A cull of marine mammals may affect the abundance of fish, and hence the data on which the assessment is based, and thereby affect future TACs, which will in turn affect the future abundance of fish, and so on. Modelling the effect of the cull on the fish stock therefore requires modelling the process of assessment of the fish stock and the calculation of TACs. This will be possible provided that the procedure for setting the TAC is sufficiently well defined. It will also involve modelling the data on which the fish stock assessments are based (Section 3.3.7 - Data collection submodels).
(iii) In the case of fish stocks subject to TACs or similar management restrictions, but where the procedures for setting TACs or other management measures are not sufficiently well defined for them to be meaningfully included in a model, it will not be possible to model the direct impacts of a marine mammal cull on fish catches. In this case, all that can be done is to model the impact of a cull under a range of reasonable assumptions about catch levels over a limited period, to ascertain the effect of the cull on the final level of the fish stock. The assumption is that the higher the level of the fish stock at the end of the period, the greater the potential future catch levels, even though the relationship between the two cannot be quantified. An assumption of this approach is that levels of TAC over this period are independent of whether the cull takes place. This assumption will tend only to be realistic for relatively short periods, say 5-10 years.

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Data collection submodels
Some of the cases mentioned above involve simulations in which management actions are regularly taken on the basis of the data collected to date, such as the number of mammals to be culled, or the TAC to be set for the fishery. In such cases, the type of data to be collected needs to be specified, along with their precision, and the method by which they will be used to determine the management action. All such data are subject to a certain degree of random error. For each type of data, a submodel is required to specify the nature of the random error (e.g. log-normal distribution) and its extent (e.g. coefficient of variation). It is well established that the predicted performance a fishery management strategy based on data subjected to realistic levels of error can be very different from the performance that would be predicted on the basis of perfect data. This aspect of the modelling exercise can be one of the most important.
3.4. Choice of parameter values
The parameter values for each of the submodels should be chosen to be consistent with what is known about the various components. There are two main types of uncertainty in parameter values or model structure:
(i) Uncertainty that is not readily quantifiable - for example, a choice between alternative model structures, or the setting of a parameter value for which little or no quantitative data are available. A common example of this kind of uncertainty is the relationship between predator diet and prey availability (functional response).
(ii) Quantified uncertainty - for example, where an estimate, with an associated variance, is available for a parameter value. Examples of this kind of uncertainty include the current abundance of the marine mammal and the fish stocks.
Uncertainty which is not readily quantifiable can be handled by choosing a reasonable intermediate model or parameter value for the base case scenario, together with one value or model on either side of this, chosen so as to span the possible or plausible range for the parameter value or model. Uncertainty which is quantifiable can be handled in a similar way, by taking, say, the 95% upper and lower confidence limits of the parameter value as the end of the range of likely values.
The advantage of this approach is its transparency and conceptual simplicity; a disadvantage is that it requires constructing two additional scenarios for each uncertain parameter value, thus increasing the overall number of scenarios which will later have to be taken into account in reaching an overall verdict.
Uncertainty which is quantifiable can alternatively be handled by defining a probability distribution for the value of the uncertain parameter (for example, log-normally distributed about the point estimate of the parameter). For each random replicate of each scenario, a value for this parameter will be chosen randomly from the defined probability distribution.
Since it will normally be necessary to run multiple replicates of each scenario anyway (because of the presence of inherently random or unpredictable factors, such as fish stock recruitment), the second method of handling parameter uncertainty requires no additional scenarios, nor even additional replicates of each scenario. A disadvantage is that it will not be readily transparent which source of uncertainty is contributing substantially to the overall uncertainty in the final result. Hence it will normally be necessary to employ a combination of both methods.

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More sophisticated approaches for handling parameter uncertainty are available, including Bayesian conditioning, which can be used provided that the required expertise is readily available and provided that this does not result in the analyses becoming bogged down in the complexities of these methods.
Plausible ranges for some unknown parameters, such as the potential growth rates of different species can be estimated from general allometric relationships where specific data are lacking.
3.5. Performance measures and other output statistics
The simulation model will generate a large number of results for each replicate of each scenario. In order to interpret and use the results, it is necessary to focus on a few well-defined output statistics.
The predicted range of possible outcomes can often be quite broad, reflecting the level of uncertainty in the model. Hence it is useful to focus on the differences between the results of the cull and no-cull variants of each scenario. Even if the overall range of outcomes is very broad, useful conclusions can nevertheless be drawn when, for example, the effects of a cull are all in a consistent direction.
Three main types of output statistics need to be identified and defined:
(i) For each objective of the cull, specific, numerical performance measures need to be defined for use in quantifying the merits or demerits of the proposed cull relative to those objectives. For example, if an objective of the cull is to increase the level of catch from a given fishery, a suitable performance statistic would be the difference (between the cull and no-cull scenarios) in the mean annual level of catch from that fishery over a period of 10 or 20 years following the initiation of the proposed cull or, in the case of a single event, the year of proposed cull. The main statistic for each scenario could be the median value of this difference over the replicates, but in addition the upper and lower 5%-iles of the difference should be given, to reflect the level of uncertainty.
(ii) Performance measures should also be specified to reflect subsidiary or implied objectives of the cull, or qualifications of the main objectives. For example, if the cull is intended to reduce, but not endanger, the marine mammal population, a relevant performance statistic to reflect the latter consideration would be the lower 5%-ile of the minimum size of the marine mammal population over the cull period.
(iii) Additional output statistics are required to enhance the transparency of the results, even when they are not directly related to the objectives of the cull. For example, if an objective of the cull is to increase catches from the fishery, then, in addition to the mean level of catch from the fishery, the mean abundance of fish would be a useful supplementary performance measure. If the model predictions indicate, for example, that the cull would not appreciably improve fish catches, then it is important to see whether this result is because the model predicts that there will be little effect of the cull on the abundance of the fish stock, or whether it predicts that the fishery fails to take advantage of an enhancement of the fish stock.

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Running the model
The simulation analysis will consist of a range of scenarios: multiple replicates (typically 100) of each scenario are simulated to take account of the random variation inherent in many aspects of each scenario. Each replicate of each scenario should be run with and without the proposed cull, so that the differential benefits or losses of the cull can be discerned.
In an initial analysis, it is most convenient to define a base case scenario which contains what is considered to be the set of most likely or intermediate choices of parameter values and models. Each additional scenario differs from the base case in only one aspect: one parameter takes a value near an extreme of the plausible range.
After initial analyses have identified those parameters which appear to have substantial effects, scenarios involving combinations of alternative values of the parameters can be constructed to explore interactions. In many cases it may emerge that the results are sensitive to only a few parameters and even fewer interactions. Where this is not the case, a statistically more systematic approach to the design of the simulation ‘experiment‘ may be called for.
In programming the model, the same sets of random numbers should be used for each scenario with and without the cull, so that the differences between scenarios and between the cases of a cull and no cull are not affected by the random differences between replicates.
3.7. Interpreting the results
It is important that models be structured, parameterized and conditioned in such a way as to enhance the transparency of the results. Where possible, it is important to understand why the model produces the results it does and which factors, if any, are dominant in determining the results. If no single interaction or small set of interactions is dominating the results, then it is important to recognize this. This will help to identify priorities for further data collection.

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The effect of a marine mammal cull on fishery yields is the outcome of three main factors:
(i) the effect of the cull on the marine mammal population itself
(ii) the effect of fishing on the fish population
(iii) the "substitutability" of fish removed by marine mammals and fish removed by the fishery.
"Substitutability" here denotes the extent to which a given reduction in consumption of fish (whether of a given species or in total) by the marine mammals achieved by a cull can be reflected in increases in catches by a fishery without depleting the fish stock further or increasing the risk of stock collapse. The substitutability can also refer to the converse situation, in which increases in consumption by growing populations of marine mammals result in decreases in fishery catches. It can be useful to distinguish further between the pure ecological substitutability that could be achieved given perfect knowledge of the fish stocks, and the degree of substitution that is achievable in practice given the imperfections of fisheries assessment and management.
Model outputs should be presented in a way which enable these three factors to be separately discernable.
In cases where some of the parameters of the model are not input but are functions of other parameters, the values that the model gives to these parameters should be produced as output so that their biological feasibility can be assessed (in some cases there may exist more direct estimates or observations of the parameters against which the values can be compared).
Outputs should also be presented in ways which are meaningful to fishery scientists in terms of the ways in which they normally assess fish stocks. For example, the single-species dynamics for the fish stock of interest implied by a multispecies model (in essence the projection of that model onto one dimension) should be presented so that fisheries scientists can relate the behaviour of the model to that of the models which they normally use for stock assessment.

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Conclusions from the modelling exercise
The conclusions to be drawn from the modelling exercise depend on the pattern of results. If the results tend to go one way, this would constitute evidence in favour or against the cull, depending on the direction of the results.
For example, if the results indicate a substantial benefit from culling, in terms of the main performance statistics, in some scenarios, while in other scenarios they indicate small benefits or detriments for culling, but indicate no substantial detriments from culling in any scenario, then this would constitute evidence in favour of the cull.
A further possibility is that even within scenarios, the range of results from individual replicate runs includes both outcomes indicating substantial benefits of culling and outcomes indicating substantial detriments from culling. This situation is more likely occur if the second method of handling parameter uncertainty described in Section 3.4 is used. In such cases the conclusions to be drawn will depend on an assessment of the relative risks of culling and not culling.
3.9. Monitoring the effects of a cull
The proposed means of monitoring the effects of a cull should be evaluated along with the cull proposal. For each scenario and replicate, the model should produce as output the simulated values of the index which has been proposed to monitor the success of the cull. The simulated values should take account of the type of data on which the index is to be based, the statistical properties of these data, and the way the index is to be calculated from them.
The results of the simulation model can then be used to estimate the probability that the proposed indices will show significant effects of the cull, and, if they do, the probability that the indicated effects will be in the same direction (i.e. beneficial or detrimental) as the true effects.

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A finding that the planned means of monitoring the effects of the cull are unlikely to yield reliable results does not necessarily imply that the cull should not go ahead. There may be cases where, while it is possible to predict with reasonable confidence that a cull is likely to be beneficial, there are nevertheless inherent reasons why it will not be feasible to verify subsequently that the predicted benefits have indeed accrued. This is particularly liable to be the case in ecosystems subject to high levels of unexplained natural variability.
In some circumstances it may be desirable to develop adaptive management plans, such that subsequent management actions (e.g. to continue, stop or modify the cull) are dependent on predetermined events or effects being detected by the monitoring program.

ANNEX I Members of the Scientific Advisory Committee

Name
Country

1992

1994

Commented on protocol

Arnoldus Schytte Blix
Norway

x

.
.

Monica Borobia
UNEP

x

.

Justin Cooke
Germany

x

Enrique Alberto Crespo
Argentina

x

Michael Earle
Belgium

x

John Harwood
United Kingdom

x

Sidney Holt
Italy

x

Toshio Kasuya
Japan

x

.
.

David Lavigne
Canada

x

Andrew Read
United States

x

.
.

Jean-Paul Roux
Namibia
.

x

.

Keith Sainsbury
Australia

x

.

x

Mark Simmonds
United Kingdom

x

.

x

Tony Sinclair
Canada

x

.

x

Peter Yodzis
Canada

x

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ANNEX II Glossary
allometric relationship - mathematical relationship describing the relative increase in a part of an organism or a measure of its physiology or behaviour in relation to some other measure, usually its overall size
density dependent - those factors which influence a varying proportion of organisms in a population, depending on population density
ecological competition - use or defence of a resource by one individual that reduces the availability of that resources for other individuals, whether of the same species (intraspecific competition) or of another species (interspecific competition)
functional response - change in the rate of exploitation of prey by an individual predator as a result of a change in prey densitystock-recruitment relationship - relationship between the spawning stock biomass and the rate of recruitment of their progeny to the population

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Les changements climatiques

Des conséquences sur les baleines ?

Les changements climatiques constituent un phénomène réel et mesuré. Les températures moyennes de l’air et de l’eau de bien des régions ont changé, d’importants courants marins sont modifiés, l’Arctique et l’Antarctique fondent à un rythme inquiétant. Les conséquences des changements climatiques sur le milieu marin sont indéniables, mais personne ne sait encore quels seront les effets sur les baleines. Les scientifiques prévoient toutefois que les changements climatiques toucheront les géants de façon indirecte ; en modifiant leur habitat et en affectant leurs ressources alimentaires.
Il faut que ça brasse !

Les courants marins sont à la base de tout le réseau alimentaire ; ils permettent un mélange des couches d’eau et donc une remise en circulation des nutriments favorisant la croissance d’algues microscopiques. Ils peuvent également contribuer à créer des conditions propices à l’accumulation de plancton animal et de poissons dans certaines zones, comme les remontées d’eau froide et les polynies (zones des pôles libres de glace). Ces zones riches en nourriture sont souvent des habitats critiques pour les cétacés. La température de l’eau, la salinité et les vents régissent en partie les grands courants océaniques ainsi que les courants locaux et seront certainement altérés par les changements climatiques. Ces changements pourraient sérieusement modifier l'habitat des baleines et affecter leur garde-manger.
Le phénomène climatique El Niño est un excellent exemple qui nous permet d’envisager les conséquences des changements climatiques sur les mammifères marins. El Niño est le résultat d’une oscillation naturelle de la pression atmosphérique au-dessus de l’océan Pacifique. Périodiquement, la force des vents diminue, modifiant ainsi les courants de surface et la circulation océanique. Dans le courant de la Californie, de faibles abondances de macrozooplancton, principalement composé de krill, ont été associées aux années El Niño, années chaudes. Ces mêmes années, dans plusieurs zones du Pacifique Est, on note une diminution de proies importantes pour les otaries et les phoques. Conséquemment, la condition physiologique des femelles et le nombre de femelles gestantes chutent et le taux de mortalité chez les nouveaux-nés et les jeunes augmente. Malgré un retour aux conditions normales, les populations d’otaries et de phoques ainsi que les stocks de poissons mettent plusieurs années à se rétablir. Dans les eaux du sud de la Californie, un changement de distribution de certains cétacés a aussi été lié au phénomène El Niño et ses conséquences sur le milieu marin. Après la disparition de sa proie préférée, une espèce de calmar, la population de globicéphales tropicaux qui résidaient dans les eaux côtières de la Californie a elle aussi quitté le secteur, à la poursuite de sa proie. Au retour des conditions plus normales et au retour des calmars, une autre espèce de cétacé, le dauphin de Risso, avait déjà élu domicile dans les eaux autrefois occupées par les globicéphales.

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Pôle Nord, pôle Sud

Les pôles sont les régions de la planète les plus affectées par les changements climatiques, notamment via la fonte des glaces. On y retrouve plusieurs espèces de cétacés, qui y résident, comme les bélugas et les narvals, ou qui y migrent pour s’alimenter, dont plusieurs espèces de rorquals. En Antarctique, la température moyenne de l’air aurait augmenté de 2,5oC en 50 ans. Plusieurs chercheurs soupçonnent la réduction du couvert glacier comme responsable de la diminution de 80 % de la biomasse de krill depuis les années 1970 dans l’Atlantique Sud-Ouest, près de la péninsule Antarctique. En hiver, le krill s’alimenterait des algues microscopiques contenues dans la glace. Une perte de glace impliquerait donc une diminution des ressources alimentaires en hiver pour le krill. Les chercheurs s’inquiètent puisque le krill représente la principale ressource alimentaire des baleines de l’Antarctique, dont le rorqual bleu et le petit rorqual.
En Arctique, les changements climatiques ont déjà eu des effets majeurs ; les températures ont augmenté de 3 à 4oC en 50 ans et la calotte glaciaire est réduite de 15 à 20 % au cours des 30 dernières années. On croit que la diminution des glaces représentera une perte d’importants sites d’alimentation pour plusieurs mammifères marins, comme les bélugas et les narvals, qui profitent de l’accumulation de nourriture à la limite des glaces.
Prévoir l’imprévisible

Les mesures mises en place aujourd’hui pour gérer les populations de baleines seront-elles toujours appropriées dans un contexte où leur milieu est en transformation rapide sous l’effet des changements climatiques ? Par exemple, les aires marines protégées (AMP) sont souvent mises en place pour venir en aide à des populations de cétacés en ciblant leurs aires d’alimentation critiques. Or ces habitats pourraient être modifiés et déplacés en raison des changements climatiques. Le concept des AMP devrait-il être alors un concept mobile, adaptable, tenant compte des effets potentiels des changements climatiques ? Un autre exemple est la gestion de la chasse à la baleine. La Commission baleinière internationale fait l’objet de grandes pressions de la part de certains des pays membres pour lever le moratoire sur la chasse commerciale. Les fondements de cette requête reposent sur l’augmentation de la taille de plusieurs populations de baleines. Cependant, devant les craintes soulevées par les changements climatiques, est-il encore avisé de prôner une reprise des activités de chasse commerciale à grande échelle ?
En couplant la recherche scientifique à des outils de gestion et de protection souples, peut-être relèverons-nous un défi de taille : prévoir l’imprévisible et prendre aujourd’hui des décisions qui contribueront encore demain au rétablissement des baleines.

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Et le Saint-Laurent ?

Depuis quelques années, les chercheurs décèlent l’intrusion massive dans le Saint-Laurent d’eaux froides du Labrador. Ces eaux ont des caractéristiques (température, salinité, espèces de plancton) qui leur sont propres et peuvent modifier les masses d’eau du Saint-Laurent. D’ailleurs, depuis le milieu des années 1980, la couche intermédiaire froide du golfe et de l’estuaire du Saint-Laurent est plus épaisse et plus froide. Ces changements des caractéristiques des masses d’eau auront des conséquences sur l’écosystème du Saint-Laurent.
Il est possible que des effets de ces changements soient déjà en cours. Par exemple, on soupçonne que le refroidissement des eaux du golfe du Saint-Laurent depuis une dizaine d’années est responsable du déplacement de l’aire de répartition du capelan vers le sud du golfe. Par ailleurs, la série temporelle mesurée par Michel Harvey de l’Institut Maurice-Lamontagne (Pêches et Océans Canada) suggère une diminution importante du macrozooplancton dans l’estuaire et le golfe du Saint-Laurent, de l’ordre de 70 % entre 1994 et 2003. Des chercheurs croient que cette diminution pourrait s’expliquer par les changements climatiques et les changements des caractéristiques des couches d’eau. Et si le krill constituait 80 % du macrozooplancton en 1994, il ne représente plus que 40 % en 2003. Parallèlement, une nouvelle espèce fait son apparition en abondance dans le Saint-Laurent depuis le début des années 1990. Il s’agit d’un amphipode des eaux froides de l’Arctique du nom de Themisto libellula. Et il ne passe pas inaperçu : entre 1994 et 2003, il représente entre 2 % et 45 % de la biomasse selon les années. Il est possible que ces changements dans les communautés de l’écosystème du Saint-Laurent, un important garde-manger pour les baleines, aient des effets à long terme sur les géants.
En plus de modifier les caractéristiques des masses d’eau du Saint-Laurent, on craint que les changements climatiques modifient certains phénomènes océanographiques importants, comme celui des [url=javascript:FrmUpd(1, '1-3-4')]remontées d’eau froide[/url], un phénomène particulièrement important à la tête du chenal Laurentien, près de Tadoussac. Les eaux plus douces en provenance des Grands Lacs et du tronçon fluvial du Saint-Laurent, coulent vers l’aval et flottent sur les eaux plus salées de l’estuaire du Saint-Laurent. Ces eaux sont évacuées par le courant de Gaspé qui longe la côte Sud de l’estuaire et du golfe du Saint-Laurent. Cette « perte » constante d’eau permet de maintenir un courant montant du golfe à l’estuaire pour les eaux plus profondes. Or, on craint qu’une réduction de l’apport d’eau douce, causée par une augmentation de l’évaporation (réchauffement climatique) et une diminution des précipitations, atténue par le fait même les forces de pompage qui permettent aux eaux profondes de faire surface à la tête du chenal Laurentien. Ces eaux transportent entre autres le krill, une proie importante pour les baleines de l’estuaire du Saint-Laurent.

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Arctic Climate Impact Assessment

Seals / focas / phoques - Page 8 Interior-vig-2

An international project of the Arctic Council and the International Arctic Science Committee (IASC), to evaluate and synthesize knowledge on climate variability, climate change, and increased ultraviolet radiation and their consequences. The results of the assessment were released at the ACIA International Scientific Symposium held in Reykjavik, Iceland in November 2004.

The Arctic Council is a high-level intergovernmental forum. The members are Canada, Denmark, Finland, Iceland, Norway, the Russian Federation, Sweden, and the United States of America. IASC is a non-governmental organization that facilitates cooperation in all aspects of arctic research in all countries engaged in arctic research and in all areas of the arctic region.

The ACIA Secretariat was hosted at the International Arctic Research Center at the University of Alaska Fairbanks. Funding for the Secretariat was provided by the U.S. National Science Foundation and the National Oceanic and Atmospheric Administration.

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