ICES C.M. 2001/T:08 Theme session T: Use and information content of ecosystem metrics and reference points
Life history characteristics as tools to evaluate changes in exploited fish communities Louize Hill1, Robert Mohn2, Jeremy Collie3 and Maria de Fatima Borges1 Abstract Many metrics exist for quantifying the status and change in status of fish communities over time. Each of these metrics uses a single characteristic to measure change. However, the belief that a single characteristic can capture all relevant change is likely to be naïve, and such an approach may not show important changes. Thus using a number of characteristics together could give a better picture of any change, and provide a tool that could warn against deleterious changes in the fish community. Based on a series of hypotheses about how the life history characteristics of individual fish species determine their sensitivity to fishing, we develop aggregate indices and study how they respond in a number of systems. This approach is of particular use in areas where extensive survey data sets are not available, and data-demanding ecosystem models cannot be applied. In all areas, for most species, several life history characteristics are known, at least as global values. Here we use a number of species characteristics for fish sampled during surveys, weighted with annual abundance to look at both temporal and spatial evolution. Changes in fishing effort are also considered. Potential explanations for any observed changes are given both in terms of fishing impacts and environmental influences. Conclusions are made about the utility of these types of metrics as descriptive and predictive tools. Introduction There is increasing concern about the effects fishing activities have on fish populations, not only directly as fishing mortality, but also indirectly from knock on effects, species interactions, etc (ICES 2000; ICES 2001). Changes at both individual and at assemblage levels have been reported (Quéro and Cendrero 1996; Jennings and Kaiser 1998; Jennings et al. 1998; Rochet 1998; Rogers and Ellis 2000), and it is important to be able to quantify these changes. For this reason a number of ecosystem metrics have been developed, these usually consider a particular characteristic to indicate whether there has been a change in the studied system, such as: size spectra (Rice and Gislason 1996; Gislason and Rice 1998; Bianchi et al. 2000), trophic level indices (Pauly et al. 2001) and species diversity (Greenstreet et al. 1999; Rogers et al. 1999; Bianchi et al. 2000; Blanchard 2001), etc. However, it is not enough for a metric only to be able to assess whether there has been a change in the ecosystem. It must also be able to determine the consequences of the change, and identify necessary mitigation measures. It should also provide reference points that can then be used as a basis for scientific advice for ecosystem management. Thus, using a single life history characteristic as an indicator could be problematic, the real causes of observed changes may be masked, for example, the overall Lmax may decrease in an assemblage because of an invasion of a small species, rather than due to a decline in larger species. It is therefore advisable to use a metric based on a number of characteristics. Fishing will affect different species or individuals of a
1
Louize Hill and Fatima Borges, DRM, IPIMAR, Avenida de Brasilia, PT-1449-006 Lisbon, Portugal. e-mail:
[email protected],
[email protected]. Robert Mohn, Dept. of Fisheries & Oceans, Bedford Institute of Oceanography, P.O. Box 1006, Dartmouth, NS B2Y 4A2, Canada. email:
[email protected]. 3 Jeremy Collie, University of Rhode Island, Graduate School of Oceanography, Narragansett, RI 02882, USA. e-mail:
[email protected]. 2
1
same species to a greater or lesser extent, and a species’ life history characteristics are one of the factors that will make the species more or less sensitive to fishing. A number of life history characteristics are readily available for most species (either from the literature and biological studies or as global values from data bases such as FishBase (Froese and Pauly 2000; http://www.fishbase.org/)). Hence, life history characteristics can be used to subscribe a species a priori to a group that is either sensitive or not to fishing. Because of the availability of these data, metrics based on life history characteristics could also provide an alternative to more data demanding metrics in areas where there is a lack of input data. Some such studies using life history characteristics have already been carried out in the North Sea (Walker and Hislop 1998; Jennings et al. 1999a; Rogers and Ellis 2000), and in the north-east Atlantic (Jennings et al. 1998).
This work expands on preliminary analyses carried out at the Working Group on Ecosystem Effects of Fishing 2001 meeting (ICES 2001). A variety of combinations of life history characteristics are used in order to see how they behave in conjunction and different types of analysis are carried out. Before the analyses were carried out, a number of hypotheses about the effects of fishing on these characteristics were formulated. It was assumed that those species that would be more sensitive to fishing disturbance are: large species (high Lmax, Linf also Lmat); slow growing species (k from von Bertalanffy equation); species that mature later and larger (high Amat and Lmat); species with low fecundity and life-time reproductive output and species at a high trophic level. Other factors that may show change include: species richness and species diversity (both spatially and temporally) as well as the spatial distribution of a given (set of) species. In this study we consider data from the Portuguese continental waters in the Atlantic. There are two distinct trawl fisheries in Portugal, one targeting fish species and the other targeting crustaceans. The fish trawl fishery is a multi-species fishery, and the main species caught by this fishery are Atlantic horse mackerel (Trachurus trachurus) (44% of landings in 1999), blue whiting (Micromesistius poutassou) (16%), hake (Merluccius merluccius) (7%) and mackerel (Scomber scombrus) (6%). Considering landings over the past 50 years (Figure 1), it can be seen that there was a large (5-fold) increase in landings in the early 1970’s, but since then there has been a steady decrease to present levels (17 thousand tonnes in 1999). Effort data (as hours trawling per year) is available for the period 1950 to 1987 (Cardador 1988), and it can be seen that during this time effort follows the same pattern exhibited by landings. Methods and results Compilation of data set: Survey data: bottom trawl research surveys have been carried out annually on the shelf and slope up to a depth of 500-750m during summer and autumn in continental Portuguese waters since 1979 and are well described in Cardador et al. (1997). Data from the autumn (fourth quarter) surveys for 1982, 1985 and 1987 and from 1989 to 2000 are used here. Species life history characteristic data: during these surveys a total of 219 different fish species were caught. Thirty-nine species were excluded from the data set as one or more species characteristic was missing. These were all rare species, representing less than 1% of the total number of all individuals in the data set. A further 2 species were excluded; these are snipefish (Macroramphosus sp.) and boarfish (Capros aper). These small species (Lmax 30 and 20 cm respectively) have both become extremely abundant over the 2
past decade (presently representing respectively 76% and 8% of the total number of individuals in the studied period). They are highly migratory species, following processes that are not fully understood, but that are believed to be related to climatic factors.
The life-history characteristics for the remaining 178 species were compiled. The variables available for all these species were Lmax, trophic level, lifestyle and habitat. The values used are global ones (extracted from FishBase (Froese and Pauly 2000) or taken from Whitehead et al. (1984)) and are not specific to the studied region. Lmax is the maximum size recorded for an individual of that species. Trophic level is taken directly from FishBase (Froese and Pauly 2000), where it is defined as “the position in the food chain, determined by the number of energy-transfer steps to that level”. The definition goes on to specify that “thus, a primary consumer which consumes ‘mainly plant/detritus’ (herbivores) may have values of trophic level between 2.0 and 2.19; secondary, tertiary, etc. consumers which consume ‘mainly animals’ (carnivores) may have trophic levels equal to or greater than 2.8; and fish which are partly herbivore and partly carnivore, i.e., omnivores which consume ‘plants/detritus + animals’ may have trophic levels between 2.2 and 2.79”. Trophic level is calculated from diet information, or from ECOPATH models of the ecosystems in which the species live. Lifestyle and habitat are modified from classifications used in (Daan 2001b) and describe where in the ecosystem each species is most likely to be found (Table 1). Habitat is the water depth at which the fish is found and lifestyle is where in the water column the fish is found during the adult stage of its lifecycle. These classifications are obviously not exact descriptions of where the fish is found, shelf fish may venture onto the slope, slope fish may venture onto the shelf, and oceanic ones can swim over the shelf and the slope. Analyses Temporal evolution of Lmax and trophic level: For these analyses all the stations are separated into 3 geographic zones (north, centre and south) at 39.5 and 37.1 degrees latitude, and into 2 depth strata (less than and more than 150m). This gave 6 assemblages that were used for these analyses. The criteria for these choices were largely taken from Gomes et al. (2001). For each year and for each of these groups the total number of individuals of each species (after being scaled up to the number of individuals per hour for each haul if necessary) was calculated. In order to evaluate the temporal evolution of Lmax and trophic level characteristics, and to assess whether these characteristics can be used to evaluate if there has been a change in the ecosystem, the weighted average of Lmax and of trophic level were calculated for each year according to the following equation and plotted against time (Figure 2):
W
= ∑i ( N / K ) • lh s
lh
where lh is the life history characteristic, s is the species, N is the number of individuals and K is the station. It can however be seen that there is no clear trend of either Lmax or trophic level decreasing (which would be expected in a heavily exploited system), or even increasing. These results are not surprising in the shallow assemblages since the coastal areas up to a depth of 100m are mainly used as nursery areas, and autumn is the recruitment season for many species such as sardine, horse mackerel or hake (Borges 1983; Borges 1984; Cardador 1984; Gomes et al. 2001). Thus, variability in Lmax and in the trophic level in these shallow assemblages is mainly due to fluctuations in the strength of year classes. It would therefore be more 3
appropriate to concentrate these analyses the habitat of adult fish, i.e. approximately the deep assemblages (Borges and Gordo 1991; Murta and Borges 1994; Cardador 1995). It can be seen that for the deeper assemblages (Figure 2), the average trophic level remained relatively constant over the studied time and in all three regions. This may be expected in a system in which the adults are not heavily exploited, and is thus coherent with the decrease in effort observed over the past 25 years (Figure 1). Unexpectedly, in the south deep assemblage Lmax seems to behave inversely to trophic level, this may be indicative that factors other than fishing pressure are influencing the structure of this assemblage. To try and identify what these factors may be the data were further explored. Species richness, as the average number of species per survey station per year was then plotted (Figure 3). In this figure it can be seen that in all assemblages there is an increase in the number of species per station over time. This may be considered to be caused by improved identification of rare species, which has been seen to be problematic in research surveys in other areas (Daan 2001a), but IPIMAR identification criteria have been standard over time (Cardador and Borges 1998). This therefore seems unlikely to be the cause for this increase in the number of species. A probable cause is the immigration of new species to the system, essentially from further south, as average annual temperatures of the upper 300m of the North Atlantic have been observed to have increased by about 0.5ºC between 1984 and 1999 (Brander et al. 2001). These authors have reported northward shifts in the distribution range of African warm-water species to Portuguese waters, as well as a northward increase in the distribution of typically southern Portuguese species such as chub mackerel (Scomber japonicus), jack mackerel (Trachurus picturatus) and snipefish. Survey results confirm the fact that these shifts were more pronounced in the 1990’s than in the 1980’s. To further explore the behaviour of average Lmax within the studied assemblage, the cumulative frequency distribution was calculated for all assemblages combined (in order to have a higher number of samples), and for alternate years. The results were then bootstrapped (Figure 4). This analysis shows that although there is no change in the average Lmax over time (Figure 2), there is marked variation in the distribution of Lmax between years. For example, in most years there is a peak of abundance around 50 cm, however in some years (e.g.: 1993, 1997), the peak is at 70 cm. In order to better understand these results, and to establish which species contribute most to these trends, the relative abundance of the 10 most important species each year was compared (Figure 5). From this figure it can be seen that most years blue whiting (Micromesistus poutassou) is dominant (Lmax = 50 cm), whereas in 1993, when there is a peak of larger fish it is the mackerel species (jack mackerel, Lmax = 60 cm, chub mackerel, Lmax = 60 cm, and mackerel (Scomber scombrus), Lmax = 64 cm) that are dominant. Regression analyses: For these analyses the data were considered on a species-by-species basis rather than in aggregate, and all four life history characteristics, Lmax, trophic level, habitat, and lifestyle were analysed. For each species and year the numbers over zone and depth were summed. This resulted in 178 species and 15 years. Then, those species that had been caught in three or more years were selected, and zeros were added in years when they weren't caught. For each of the remaining 126 species abundance against year was regressed log (x+1) and the slope and standard error of the slope was recorded. The slope indicates whether that
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species has increased or decreased over time. The distribution of slopes is roughly normal and the mean is close to zero (Figure 6). The standard error of the slope is not correlated with the slope itself. Next, in order to evaluate which of the four life history characteristics affect the slope of these regressions, general liner models were carried out on the slopes, weighted by the inverse of the standard error of the slope and regression trees were fitted (Table 2, Figure 7). From this table and figure it can be seen that habitat explained most of the variance. Habitat describes where in the water column each species is most commonly found, and is divided into shelf (1), slope (2) and oceanic (3) classifications. It is therefore perhaps not so surprising that this factor has the greatest effect on the results, as these data are from bottom trawl surveys that do not sample the ecosystem exhaustively, and that target demersal shelf and slope species. Spatial Analysis: For these analyses only data for the elasmobranch (31 species) and gadid (21 species) species were used. These groups were chosen as a subset of the total data, in order to keep the data set small, because of their contrasting life history strategies. Because of both their reproductive strategies and their morphology, elasmobranches a priori would be expected to be more susceptible to fishing (Walker and Heessen 1996; Walker and Hislop 1998). Similarly to the other analyses, the time series included data for 1982, 1985, 1987 and 1989-2000. The data used for these analyses were year, latitude-longitude, depth, species name (and code), number and biomass caught and Lmax. Prior to the analysis the data were aggregated to tenth of a degree squares. Three spatial metrics were calculated for these species groups, and the results were compared to Lmax weighted abundance over time. The metrics used are the anomaly of the centre of mass of the community, an index of contagion and an index of overlap. These metrics are contrasted in that the centre of mass is a large scale attribute, while the index of contagion and index of overlap are fine scale within the overall distribution. Complete details of these metrics are available in (ICES 2001), but they can briefly be described as follows: •
Anomaly of the centre of mass of the distribution of the community. This metric is calculated by first computing the centre of mass for each year and then summing over the species or community under consideration. The average centroid over the time series is found and then the distance (in nautical miles) from each annual point to the average is found giving an annual anomaly.
•
Index of contagion. This index is the number of neighbours within a set radius, which is specified for each species. For a highly mobile pelagic fish the radius would be large, while it would be smaller for a less mobile species. If there are 2 individuals in adjacent units then they are defined to be neighbours, if a unit separates them, they have no neighbours. This is defined as contagion. The metric records the number of neighbour “pairs” within the studied area. As contagion is probably more important on the species level, this metric is computed for each species in the relevant group and is then summed for all species under consideration.
•
Index of overlap. This index is proposed to indicate the displacement of a resource from its traditional, pristine or desired distribution. A reference year (or average of years) is chosen and it is then compared to the other years in a time series. As the data are aggregated onto a fine scale, in these examples 0.1 degree, grid and the metric only checks whether the same squares are occupied 5
as in the reference distribution. The index is the fraction of occupied grids in the reference distribution that are shared. First, in order to observe general trends in the data sets, weighted abundance was plotted against average Lmax for the gadid and elsmobranch groups separately (Figures 8a and 9a). For gadids abundance fell during the studied period, but Lmax showed little dynamics, except for a small blip in 1992. For the elasmobranches there was an increase in abundance during the 1980’s, and a spike in recruitment in 1996. This spike did not affect the average Lmax, presumably because animals near the mean Lmax caused it. In order to compare the differences in impact on large and small species, the data sets were divided into species of more and less than 100cm (Figures 8b and 9b). For the gadids it is clear that large species were much more impacted during the time series than small ones. The weighted Lmax failed to pick up this event, probably because the large species always constituted a very small proportion of the total group. For the elasmobranches it can be seen that the increase during the 1980’s was of large species, whereas small fish dominated the blip in 1996. Figure 10 shows the abundance and the anomaly of the centre of biomass. For the gadids although the anomaly seems to be in opposite phase to the abundance, the metric does have the same decreasing trend as the abundance. The anomaly does not seem to be in phase for the elasmobranches either. It records an increase in 1985, which is not reflected in the abundance, whereas when there is a large change in abundance in 1996, the anomaly does not react. At present this metric raises more questions than it answers, and more work needs to be carried out to further investigate its potential as a predictive tool. Figure 11 shows the results from the indices of contagion and overlap. For gadids overlap seems quite stable, and doesn’t change a great deal. The contagion index is in opposite phase with abundance. For the elasmobranches both the indices are quite dynamic, though neither follows abundance clearly. One interesting observation is the fact that in 1996, when abundance increased considerably, both the metrics showed an increase, suggesting that this increase was localized.
Discussion From these preliminary analyses the metrics tested here do not seem to lead to clear-cut, conclusive results. Furthermore, the metrics show different variability in each assemblage. These observations may be explained by a number of reasons. The shallow assemblages will have a higher variability than the deep ones because of the spatial distribution of the different ontogenic stages of the species present (Gomes et al. 2001). Many of the most abundant species found in continental Portuguese waters occur in both the deep and shallow assemblages. Many of these species, such as hake and horse mackerel, form nurseries in shallower waters on the shelf during the autumn recruitment season (Borges 1983; Borges 1984; Borges 1988; Cardador 1995), according to the dynamics and timing of upwelling conditions in the Portuguese region (Santos et al. 2001). Upwelling systems and fish dynamics have been studied worldwide (Cushing 1975; Bakun 1996). Variance in the Lmax variable between years in the shallow assemblages is therefore largely due to the variability in year-class strength. Thus, if these analyses had been carried out using the summer bottom trawl survey data the results 6
may have been very different. The Lmax metric will therefore be more conclusive in the analysis of the deep assemblages, or of the large (adult) individuals, as can be seen in figures 2 and 8b. Another reason is the fact that this is not a heavily exploited ecosystem, from figure 1 it can be seen that this ecosystem has been decreasingly heavily exploited over the past 25 years. This also means that the time series is very short, as it does not include the pristine condition, and starts immediately after a period of particularly intense exploitation (during the 1970’s). A further reason for the lack of conclusive results from these metrics may be the change in faunal composition over time. It has been seen that species richness has increased in the whole area, in particular in the southern and central assemblages (Figure 3) during this period. (Brander et al. 2001) reported the immigration of African species to the South of Portugal (Algarve) due to increasing ocean temperatures. These authors also reported a northerly displacement of chub mackerel and of jack mackerel as well as the species we eliminated from the analyses, snipefish and boarfish. In a future analysis it may therefore be more conclusive to screen and remove the new species before testing the hypothesis to eliminate the problem of these invasions. Finally, the metrics may not have behaved as we hoped because of the relatedness of the characteristics, e.g.: correlation between Lmax and trophic level. Nevertheless, other studies have shown that life history characteristics can be useful tools for predicting the susceptibility of a species to disturbance in the ecosystem (Jennings et al. 1998; Walker and Hislop 1998; Jennings et al. 1999b; Rogers and Ellis 2000; ICES 2001). Furthermore the changes in life history characteristics caused by fishing are amongst the longest lasting effects, and are difficulty reversible. Finally, metrics based on life history characteristics should be fairly easily applicable in areas where extensive data on the biology of all species is not available, as global values of life history characteristics can be found for most species (180/219 in Portugal) in the literature and in electronic databases (eg: FishBase (Froese and Pauly 2000)). These analyses were preliminary, and exploratory, and at this stage raised more questions than they answered. This study therefore opens the way for further work that should include the comparison of how these metrics behave in other (more heavily exploited) ecosystems. It will also be interesting to further explore the behaviour of these metrics for this ecosystem by “fine-tuning” the data set. Actual values for some of the life history characteristics could be used instead of the global values, eg: Lmax from length sampling. This information, combined with Lmat, could also be used to remove juvenile fish from the data set. Finally, explanations for any observed changes in terms of both fishing impact and environmental influence should be looked for. After these further studies it will be possible to assess the utility of these metrics as descriptive and predictive tools. Acknowledgements Thanks to all members of WGECO present at the 2001 meeting, where this work was conceived, and in particular to Gorka Sancho, Alan Fréchet, Gerjan Piet and Niels Daan for assistance in compiling the life history characteristics. Thanks also to IPIMAR and the coordinators of the EU FAR project MA-1-203 and the SESITS Study Contract for allowing us to use the IPIMAR bottom trawl autumn surveys data, and in 7
particular to Fatima Cardador. Finally special thanks Henrik Gislason for hosting the 1st author at DIFRES, and to Anders Nielsen and Uffe Høgsbro Thygesen for help with statistical analysis and in using R software. The first author was funded by a grant from the Portuguese Foundation for Science and Technology (FCT).
References Bakun, A. 1996. Patterns of the Oceans: Ocean processes and marine populations dynamics. California Sea Grant, La Jolla, CA and Centro de Investigaciones Biologicas del Noroeste, La Paz, BCS. 323 pp. Bianchi, G., Gislason, H., Graham, K., Hill, L., Jin, X., Koranteng, K., Manickchand-Heileman, S., Paya, I., Sainsbury, K., Sanchez, F., and Zwanenburg, K. 2000. Impact of fishing on size composition and diversity of demersal fish communities. ICES Journal of Marine Science 57:558-571. Blanchard, F. 2001. Une approche de la dynamique des peuplements de poissons démersaux exploités: une analyse comparée de la diversité spécifique dans le golfe de Gascogne (océan Atlantique) et dans le golfe du Lion (mer Méditerranée). Aquatic Living Resources 14:29-40. Borges, M. F. 1983. Recruitment indices of horse mackerel (Trachurus Trachurus L.) based on young fish surveys in the Portuguese waters (Division IXa). ICES CM 1983/H:40, 33pp. Borges, M. F. 1984. Evaluation of the results on horse mackerel of a series of young serveys in the Portuguese waters (Division IXa). ICES CM 1984/H:26, 44pp. Borges, M. F. 1988. An investigation of the state of the stock of scad (Trachurus trachurus L) in Iberian waters. Instituto Nacional de Investigação das Pescas, Lisbon, Portugal 145pp. Borges, M. F., and Gordo, L. S. 1991. Spatial distribution by season and some biological parameters of horse mackerel (Trachurus Trachurus L.) in the Portuguse continental waters (Division IXa). ICES CM 1991/H:54, 16pp. Brander, K., Blom, G., Borges, M. F., Erzini, K., Hendersen, G., MacKenzie, B., Mendes, H., Santos, A. M. P., and Toresen, R. 2001. Changes in fish distribution in the Eastern North Atlantic; are we seeing a coherent response to changing temperature?. ICES Journal of Marine Science. Edinburgh Symposium Proceedings (submitted). Cardador, F. 1984. Recruitment indices of hake in Portuguese continental waters (Division IXa). ICES CM 1984/G:26, 26pp. Cardador, F. 1988. Estratégias de exploração do stock de pescada (Merluccius merluccius L.) das aguas Ibero-Atlânticas. Efeitos em stocks associados. Instituto Nacional de Investigação das Pescas. 30pp. Cardador, F. 1995. Factors influencing the distribution and abundance of hake (Merluccius merluccius) in the Portuguese waters (ICES Div. IXa) based on groundfish surveys data. ICES CM 1995/G:20, 14pp. Cardador, F., and Borges, L. 1998. Manual dos cruzeiros demersais do IPIMAR. N/I Noruega. Instituto Nacional de Investigação das Pescas 12pp. Cardador, F., Sanchéz, F., Pereiro, F. J., Borges, M. F., Caramelo, A. M., Azevedo, M., Silva, A., Pérez, N., Martins, M. M., Olaso, I., Pestana, G., Trujillo, V., and Fernandez, A. 1997. Groundfish surveys in the Atlantic Iberian waters (ICES Divisions VIIIC and XIa): history and perspectives. ICES CM 1997/Y: 08, 30pp. Cushing, D. H. 1975. Marine Ecology and Fisheries. Cambridge University Press. 278 pp. Daan, N. 2001a. The IBTS database: a plea for quality. ICES CM 2001/T:03, 19pp. Daan, N. 2001b. A spatial and temporal diversity index taking into account species rarity, with an application to the North Sea fish community. ICES CM 2001/T:04., 6pp.
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Froese, R., and Pauly, D. 2000. FishBase 2000: concepts, design and data sources. ICLARM, Los Baños, Laguna, Philippines. 344 pp. Gislason, H., and Rice, J. 1998. Modelling the response of the size and diversity spectra of fish assemblages to changes in exploitation. ICES Journal of Marine Science 55:362-370. Gomes, M. C., Serrão, E., and Borges, M. F. 2001. Spatial patterns of groundfish assemblages on the continental shelf of Portugal. ICES Journal of Marine Science 58:633-647. Greenstreet, S. P. R., Spence, F. E., and McMillan, J. A. 1999. Fishing effects in Northeast Atlantic shelf seas: patterns in fishing effort, diversity and community structure. V. Changes in structure of the North Sea groundfish species assemblage between 1925 and 1996. Fisheries Research 40:153-183. ICES. 2000. Report of the Working Group on the Ecosystem Effects of Fishing Activities. ICES CM 2000/ACME :02 Ref: ACFM+D+E+G, 99pp. ICES. 2001. Report of the Working Group on the Ecosystem Effects of Fishing Activities. ICES CM 2001/ACME:09 Ref. ACE, ACFM, D, E, G, 102pp. Jennings, S., Alvsvåg, J., Cotter, A. J., Ehrich, S., Greenstreet, S. P. R., Jarre-Teichmann, A., Mergardt, N., Rijnsdorp, A. D., and Smedstad, O. 1999a. Fishing effects in northeast Atlantic shelf seas: patterns in fishing effort, diversity and community structure. III. International fishing effort in the North Sea: an analysis of spatial and temporal trends. Fisheries Research 40:125-134. Jennings, S., Greenstreet, S. P. R., and Reynolds, J. D. 1999b. Structural change in an exploited fish community: a consequence of differential fishing effects on species with contrasting life histories. Journal of Animal Ecology 68:617-627. Jennings, S., and Kaiser, M. J. 1998. The effects of fishing on marine ecosystems. Advances in Marine Biology 34:201-340. Jennings, S., Reynolds, J. D., and Mills, S. C. 1998. Life history correlates of responses to fisheries exploitation. Proceedings of the Royal Society of London 265:1-7. Murta, A. G., and Borges, M. F. 1994. Factors affecting the abundance and distribution of horse mackerel (Trachurus trachurus L.) in Portuguese waters. ICES, C.M. 1994/H:20, Ref. D., 16pp. Pauly, D., Palomares, M. L., Froese, R., Sa-a, P., Vakily, M., Preikshot, D., and Wallace, S. 2001. Fishing down Canadian aquatic food webs. Canadian Journal of Fisheries and Aquatic Science 58:51-62. Quéro, J. C., and Cendrero, O. 1996. Incidence de la pêche sur la biodiversité ichtyologique marine: le bassin d'Arcachon et le plateau continental sud Gascogne. Cybium 20:323-356. Rice, J., and Gislason, H. 1996. Patterns of change in the size spectra of numbers and diversity of the North Sea fish assemblage, as reflected in surveys and models. ICES Journal of Marine Science 53:12141225. Rochet, M. J. 1998. Short-term effects of fishing on life history traits of fishes. ICES Journal of Marine Science 55:371-391. Rogers, S. I., and Ellis, J. R. 2000. Changes in the demersal fish assemblages of British coastal waters during the 20th century. ICES Journal of Marine Science 57:866-881. Rogers, S. I., Maxwell, D., Rijnsdorp, A. D., Damm, U., and Vanhee, W. 1999. Fishing effects in Northeast Atlantic shelf seas: patterns in fishing effort, diversity and community structure. IV. Can comparisons of species diversity be used to assess human impacts on demersal fish faunas? Fisheries Research 40:135-152. Santos, R. S., Borges, M. F., and Groom, S. 2001. Sardine and horse mackerel recruitment and upwelling off Portugal. ICES Journal of Marine Science 58:589-596.
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Walker, P. A., and Heessen, H. J. L. 1996. Long-term changes in ray populations in the North Sea. ICES Journal of Marine Science 53:1085-1093. Walker, P. A., and Hislop, J. R. G. 1998. Sensitive skates or resilient rays? Spatial and temporal shifts in ray species composition in the central and north-western North Sea between 1930 and the present day. ICES Journal of Marine Science 55:392-402. Whitehead, P. J. P., Bauchot, M. L., Hureau, J. C., Nielsen, J., and Tortonese, E. 1984. Fishes of the Northeastern Atlantic and the Mediterranean. UNESCO, Paris. 1473 pp.
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TABLES AND FIGURES Table 1. Classifications used to attribute species to different lifestyles and habitats. 1. 2. 3. 4. 5.
Lifestyle bathydemersal bathypelagic benthic demersal pelagic
1. 2. 3.
Habitat shelf slope oceanic
Table 2. Results from regression tree analysis Node Nº 1
Nº obs. 126
Mean 0.011
MSE 0.002
2
85
0.004
0.001
3
41
0.025
0.002
4
62
0.002
0.001
5
23
0.019
0.002
6 7
14 27
0.003 0.036
0.001 0.003
8 9 10 11 14 15
44 18 8 15 9 18
0.008 0.015 0.004 0.032 0.014 0.047
0.001 0.001 0.002 0.002 0.001 0.004
Habitat < 1.5 to left Lmax < 38 to right Trophic level < 3.95 to right Lmax < 97.5 to left Lmax < 22 to left
Primary splits Life style < 1.5 Lmax < 26.5 to right to right Life style < 4.5 Trophic level < to right 3.15 to left Lmax < 64.45 Life style < 3.5 to right to left Trophic level < Life style < 3.5 4.15 to left to right Life style < 3.5 Trophic level < to right 3.45 to left
Lmax < 64.45 to right
Life style < 3.5 to left
Habitat < 2.5 to left
Trophic level < 3.95 to right Habitat < 2.5 to left
Surrogate splits Life style < 2.5 Lmax < 316.35 to right to left Life style < 2 to right Lmax < 155 Life style < 1.5 to right to left Trophic level< 4.4 to left Life style < 4.5 Trophic level < to right 3.25 to left
Trophic level < 3.35 to left
11
Figure 1. Landings (black triangles S) and effort (white squares ) by the Portuguese trawl fleet for the period 1950 to 1999. 45000
60
40000
30000
40
25000 30 20000 15000
20
Thousand tonnes
Hours trawling (x1000)
50 35000
10000 10 5000 0
1950
0
1960
1970
1980
1990
2000
Figure 2. Average Lmax (solid line) and trophic level (dashed line) per year for each assemblage.
12
Figure 3. Species richness - average number of species per year for each assemblage.
Figure 4. Distribution of Lmax based on the results from a bootstrap analysis for the whole Portuguese continental ecosystem.
13
Figure 5. Relative importance of the top 10 species for each year. Legend contains a 3 letter code species’ and the Lmax value used. Relative importance of top 10 species and L max for each year 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 1982
14
1985
1987
1989
1991
1993
1995
1997
1999
GAA-15
ANE-20
LEC-20
PIL-25
SEE-25
ARG-35
BOG-36
SBA-36
POD-40
CTB-45
BIB-46
WHB-50
SLM-51
JAA-60
MAC-60
MAS-64
HOM-70
SBR-70
SHO-90
HKE-140
SFS-205
Others
Figure 6. Histogram of slopes from the regression analysis for the 126 species.
Figure 7. Regression tree analysis on the results of the general linear models carried out on the slopes of the regression analysis, weighted by the inverse of the standard error of the slope.
15
Figure 8. a). Gadid abundance (dashed line) and Lmax weighted by abundance (solid line). b). Gadid abundance: all individuals (solid line), Individuals greater than 100cm (long dashed line) and less than 100cm (short dashed line). Gadids
Abundance
300.0
52.0 51.0
250.0 200.0
50.0
150.0 100.0
49.0
50.0
Average Lmax
350.0
48.0
19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00
0.0
350.0
Gadids
Abundance
300.0 250.0 200.0 150.0 100.0 50.0 0.0 82 19
84 19
86 19
88 19
90 19
92 19
94 19
96 19
98 19
00 20
Figure 9. a). Elasmobranch abundance (dashed line) and Lmax weighted by abundance (solid line). b). Elasmobranch abundance: all individuals (solid line), Individuals greater than 100cm (long dashed line) and less than 100cm (short dashed line).
Abundance
12.0
250.0
Elasmobranchs
200.0
10.0 8.0
150.0
6.0
100.0
4.0
50.0
2.0
16.0
Abundance
14.0
20 00
19 98
19 96
19 94
19 92
19 90
19 88
19 86
0.0 19 84
19 82
0.0
Average Lmax
14.0
Elasmobranchs
12.0 10.0 8.0 6.0 4.0 2.0 0.0
82 19
16
84 19
86 19
88 19
90 19
92 19
94 19
96 19
98 19
00 20
Figure 10. Abundance (solid line) and anomaly of the centre of mass (dashed line) for a). Gadids and b). Elasmobranchs.
40.5
250.0
40.0
200.0
39.5
150.0
39.0
100.0
38.5
50.0
38.0
0.0
37.5 84 19
82 19
14.0
96 19
98 19
00 20
39.4
Elasmobranchs
12.0 Abundance
94 19
92 19
90 19
88 19
86 19
39.2 39.0
10.0
38.8
8.0
38.6
6.0
38.4 38.2
4.0
38.0
2.0
37.8
20 00
19 98
19 96
19 94
19 92
19 90
19 88
19 86
19 84
37.6
19 82
0.0
Centre of Mass Anomaly
Abundance
41.0
Gadids
300.0
Centre of Mass Anomaly
350.0
Figure 11. Abundance (solid line), contagion index (short dashed line) and index of overlap (long dashed line) for a). Gadids and b). Elasmobranchs.
-9.10
20000 -9.15 15000 -9.20
10000
20 00
19 98
19 96
19 94
19 92
-8.20
Elasmobranchs
-8.40
1000
-8.60
800
-8.80
600
-9.00
400
20 00
19 98
19 96
19 94
19 92
19 90
19 88
19 86
-9.40 19 84
-9.20
0 19 82
200
Contagion Index
1200
19 90
-9.30 19 88
0 19 86
-9.25
19 84
5000
Contagion Index
-9.05
25000
1400 Abundance *100 / Overlap Index
-9.00
Gadids
30000
19 82
Abundance *100 / Overlap Index
35000
17
