|Methods and data sources:
invertebrates and fishes
Structure of aquatic databases
The aquatic databases have the following overall structure:
Using a grid to divide a study area into squares is the traditional method for creating divisions, since it makes it easier to create contiguous squares. Although squares of 10 km x 10 km were used for terrestrial data since this was found to be the best scale for most terrestrial survey data, preliminary tests showed that this resolution was too fine for a marine environment. Therefore, a grid of 20 km x 20 km squares was used for commercial dragging and trawling data. In the freshwater environment, however, 10 km x 10 km squares were found to be too large and tessellation was used instead.
Tessellation consists in dividing a territory into irregular polygons (called Thiessen or Voronoi polygons). Since the polygons follow the complex contours of the shoreline, they provide complete coverage of a study area, avoiding the main problem involved in using square grids: squares that are ill fitted to the territory under study. Localized survey stations, chosen at random, were used as the centres for constructing Thiessen polygons. Polygons were generated with ArcView software using a program by Ammon (1998). Readers are invited to consult Okabe et al. (1992) and Gold (1991) for more details on the properties and applications of Thiessen polygons. Survey stations were located in Thiessen polygons to obtain local lists of species complete enough to analyze biodiversity.
Both freshwater and saltwater fishes were dealt with on the species level. The identification of invertebrates, however, often had to be done at a less precise taxonomic level, particularly in the case of insect larvae, which can often only be identified to genus. Particularly difficult or poorly known groups such as mites and nematodes were identified to class, order or family level. In the spatial analyses, we retained species records for marine invertebrates and freshwater zooplankton larvae and genus records for freshwater benthos.
Integrating a number of lists of species into a coherent database required an exhaustive review of each taxon in order to uniformly apply taxonomic revisions and correct errors. Generally, we first drew up a list of all the names of the basic taxa, with the correct name and a code to facilitate further processing. For example, records for the rainbow trout might include:
Distribution was described at two levels. For worldwide distribution, taxa were divided into broad categories (cosmopolitan, circumboreal, amphi-Atlantic or endemic to North America), emphasizing endemic North American species. For North American distribution, distribution was specified by Canadian province or U.S. state for freshwater organisms and by broad coastal marine zones for marine organisms (Arctic, Labradorean, Acadian, Virginian, Carolinian and Caribbean).
The breeding status indicates whether the species breeds in the study area.
The conservation status is based on the priority assigned to species by various committees of experts, compiled by the Quebec Department of the Environment and Wildlife (CDPNQ 1999).
A literature review of general works on the ecology of St. Lawrence species shows that many species, particularly invertebrates, are poorly known. In many cases, we had to rely on the characteristics of the genus or family. Data were compiled on the following descriptors:
Scores were also awarded for salinity tolerance (stenohaline, euryhaline), life cycle (freshwater, diadromous), maximum size attained and age at sexual maturity. The spawning substrate and time of spawning were also compiled. Data on ecophysiological constraints, such as tolerance of turbidity and extreme and optimal temperature ranges for growth and reproduction, were often scarce, however.
Statistical analyses were carried out at a number of different spatial scales. The following list gives an idea of the type of data processed and the resulting analyses:
Saltwater fishes. Survey trawls provide a fairly accurate picture of the frequency and abundance of marine species in trawlable areas, as long as the selectivity of the gear is taken into account. On the scale of the entire marine part of the St. Lawrence, each species was characterized by two criteria: frequency of occurrence (number of trawl tows in which the species was found) and mean abundance (average number of individuals by tow, when the species was observed). Species were grouped according to these two criteria, which were used simultaneously in group average clustering to determine the Euclidian distance between species.
Freshwater fishes. Owing to the use of many different gear types, often poorly described, it was difficult to rank species by frequency and abundance, since the ranking obtained depends largely on the selectivity of the fishing gear used. Since no gear type covers all species, an alternative approach based on the use of a wide variety of gear types was adopted.
There is generally a strong correlation between the size of an area and its species richness (Connor and McCoy 1979; McGuinness 1984; Levin 1996), which must be taken into account when comparing diversity between one or more units. The relationship between species richness and area was modeled using the logistical model of He and Legendre (1996):
The species-area curve for freshwater benthos was calculated as follows:
The species-area curve for freshwater fishes was calculated as follows:
The species-area curve for marine fishes was calculated as follows:
The species richness (S) observed in a unit is largely dependent on the sampling effort (n). To determine if the observed species richness was close to the actual species richness in each unit (Smax), the relationship between species richness and effort was modeled using the following equation (Colwell and Coddington 1995):
where Smax and B are parameters estimated from the cumulative richness S after n samples. The parameter Smax represents the theoretical species richness in the unit, based on the accumulation of new species as n increases. The parameter B is interpreted as the inverse of the detectability of the species. As the following table shows, at B = 5, for example, it is expected that 50% of species will be caught after five samples.
At B = 5,
Since the estimation of Smax and B by nonlinear regression is influenced by the order in which samples are added, 100 iterations of the estimates were carried out, randomizing the order of samples. In subsequent treatments, the median of Smax was used to describe the central tendency of Smax, and the 5th and 95th percentiles, for dispersion. Estimates were rejected if convergence did not occur (formation of a single mode for values of Smax).
A cluster analysis technique was used to categorize units based on the similarity of the species they contained, to reveal the main clusters as well as existing discontinuities (Legendre and Legendre 1998).
To better gauge the ecological implications of the clusters obtained, a selectivity analysis can be useful for species that have been well sampled. Groups are characterized by the species that favour or avoid areas, or which appear to be neutral (non significant test). The groups of geographic units are then compared by using the traits of species that favour them, such as salinity tolerance, degree of association with vegetation and mobility.
The list of fish species in a large river is usually compiled from a number of different surveys carried out with different types of fishing gear. This type of meta-analysis of different databases is often recommended to increase the detection of species of fish or other animals (Rodda 1993; Boulinier et al. 1998; Kidric-Brown and Brown 1993; Gibbons et al. 1997). This approach is the inverse of specific surveys with a single, selective type of fishing gear (FAO 1975), which provides quantitative data on only some of the species present. In this study, first we grouped together gear types that generally detected the same species. Then, we tested the hypothesis that lists of species obtained without taking into account the gear type were more exhaustive than lists prepared with data from specific gear types, as well as the hypothesis that the former provided better spatial coverage.
The database on freshwater fishes in the St. Lawrence River consists of nearly 14,000 samples obtained between 1928 and 1997, most dating from between 1960 and 1997. The samples, georeferenced by longitude and latitude, can be grouped into geographic units (polygons) of variable area associated with a list of species by gear type and an overall list of species regardless of gear type. These lists do not take account of the potential temporal variations that could have occurred during the period covered.
The information on fishing effort and the type of fishing gear was often scarce or incomplete. Therefore, gear types were classified based on how well they were described and the frequency of use, but not according to fishing effort, for which the data were not complete enough. The database contains records of 39 types of fishing gear that detected a total of 102 fish species. This result is very similar to the species richness found in the St. Lawrence by Underhill (1986), who listed 98 species. Consequently, it can be concluded that, when taken together, the 39 types of gear describe the regional species richness of fishes in the St. Lawrence.
The first step consisted in identifying well-described gear types that caught more or less the same species, by calculating the probability of detecting species for the main gear types. To do this, stations were grouped into 1 km² squares. For each gear type, we calculated the number of squares where the gear was used and where the species was present, since the species was detected by one of the gear types used in the squares. The probability of detecting a species with a given gear type was calculated as:
- Beach seine type (beach seine, gillnets with a mesh size of 25 mm and electrofishing);
The beach seine type detected 91 out of 102 species, far more than the 38-mm gillnet type (54 species) and 102-mm gillnet type (41 species). The group of unspecified gear types represented 3279 samples and detected 98 species.
The division of the study area by tessellation was used to compare estimates of Smax (theoretical species richness of each unit) based on three groups of samples: those obtained with the beach seine gear type, those obtained with 38-mm gillnets and all samples regardless of gear type. Samples obtained with 102-mm gillnets were eliminated since this gear type is relatively uncommon and detects very few species.
As the figure shows, the estimates were strongly positively correlated. However, it must be remembered that samples obtained with the beach seine gear type are a subset of the set of all samples obtained regardless of gear type. The scatter of the estimates of species richness along the diagonal line represents the additional variation resulting from the addition of gear types other than the beach seine type.
Similarly, samples obtained with 38-mm gillnets were compared with those obtained with all (unspecified) gear types, showing a strong positive correlation. The comments on method made in the previous paragraph also apply in this case.
These results show that a range of unspecified or poorly described gear types can be used to estimate local species richness if this results in a substantial gain in spatial coverage.
The use of all samples regardless of gear type provides good spatial coverage, as the following table shows. By limiting the analysis to the beach seine type, we would only obtain reliable species richness estimates in 120 tessellation units covering 91 species. As its name indicates, the beach seine can only be used near the shore and not in deep water. Gillnets with 38-mm mesh are more versatile in this respect, providing reliable estimates in 167 units; however, they are much more selective, detecting only 54 species. Samples from all gear types provided coverage of 247 units and 98 species. Four other species were cited in the database (to make the total of 102 species) but were not georeferenced.
The list of 102 species in the freshwater part of the St. Lawrence was compiled from nearly 14,000 samples obtained by 39 different kinds of fishing gear. Despite the large number of samples, no single type of gear was able to detect all the species. The most important conclusion to be drawn from our calculations is there is a strong correlation between species richness detected by specific gear types and that compiled without taking account of gear type. Therefore, the second option was retained, since the analyses were based on the occurrence of species in geographical units rather than on abundance. Due to this approach, we were able to take advantage of the large number of samples for which the gear type was unspecified or poorly described, which provided significantly better spatial coverage.