Does Long-Term Macrophyte Management in Lakes Affect Biotic Richness and Diversity?

We hypothesize that the richness and diversity of the biota in Lake Moraine (42°50’47”N, 75°31’39”W) in New York have been negatively impacted by 60 years of macrophyte and algae management to control Eurasian watermilfoil ( Myriophyllum spicatum L.) and associated noxious plants. To test this hypothesis we compare water quality characteristics, richness and selected indicators of plant diversity, zooplankton, benthic macroinvertebrates and fish in Lake Moraine with those in nearby Hatch Lake (42°50’06”N, 75°40’67”W). The latter is of similar size and would be expected to have similar biota, but has not been subjected to management. Measurements of temperature, pH, oxygen, conductivity, Secchi transparency, calcium, total phosphorus and nitrites + nitrates are comparable. Taxa richness and the diversity indices applied to the aquatic macrophytes are similar in both lakes. (PDF has 8 pages.)

Can a change in the spawning pattern of Argentine hake (Merluccius hubbsi) affect its recruitment?

Argentine hake (Merluccius hubbsi) inhabit waters of the Southwest Atlantic Ocean between 22° and 55°S, at depths ranging from 50 to 500 m (Cousseau and Perrota, 1998). This species has historically been among the more abundant fish resources in the Argentine Sea, where its biomass has ranged between one and two million metric tons annually since 1986 (Aubone et al., 2000). In this area, there are two identified fishing stocks, limited by the 41°S parallel. The southern group (Patagonian stock) is the more important with an abundance of about 85% of the total biomass estimated for this species in 1999 (Aubone et al., 2000). During the late 1990s, the spawning biomass of both stocks and their recruitment indices declined drastically, both of which were attributed to an increase in exploitation (Aubone et al., 2000).

Multiscale analysis of factors that affect the distribution of sharks throughout the northern Gulf of Mexico

Identification of the spatial scale at which marine communities are organized is critical to proper management, yet this is particularly difficult to determine for highly migratory species like sharks. We used shark catch data collected during 2006–09 from fishery-independent bottom-longline surveys, as well as biotic and abiotic explanatory data to identify the factors that affect the distribution of coastal sharks at 2 spatial scales in the northern Gulf of Mexico. Centered principal component analyses (PCAs) were used to visualize the patterns that characterize shark distributions at small (Alabama and Mississippi coast) and large (northern Gulf of Mexico) spatial scales. Environmental data on temperature, salinity, dissolved oxygen (DO), depth, fish and crustacean biomass, and chlorophyll-a (chl-a) concentration were analyzed with normed PCAs at both spatial scales. The relationships between values of shark catch per unit of effort (CPUE) and environmental factors were then analyzed at each scale with co-inertia analysis (COIA). Results from COIA indicated that the degree of agreement between the structure of the environmental and shark data sets was relatively higher at the small spatial scale than at the large one. CPUE of Blacktip Shark (Carcharhinus limbatus) was related positively with crustacean biomass at both spatial scales. Similarly, CPUE of Atlantic Sharpnose Shark (Rhizoprionodon terraenovae) was related positively with chl-a concentration and negatively with DO at both spatial scales. Conversely, distribution of Blacknose Shark (C. acronotus) displayed a contrasting relationship with depth at the 2 scales considered. Our results indicate that the factors influencing the distribution of sharks in the northern Gulf of Mexico are species specific but generally transcend the spatial boundaries used in our analyses.

Does the size of subsamples taken from multispecies trawl catches affect estimates of catch composition and abundance?

Although subsampling is a common method for describing the composition of large and diverse trawl catches, the accuracy of these techniques is often unknown. We determined the sampling errors generated from estimating the percentage of the total number of species recorded in catches, as well as the abundance of each species, at each increase in the proportion of the sorted catch. We completely partitioned twenty prawn trawl catches from tropical northern Australia into subsamples of about 10 kg each. All subsamples were then sorted, and species numbers recorded. Catch weights ranged from 71 to 445 kg, and the number of fish species in trawls ranged from 60 to 138, and invertebrate species from 18 to 63. Almost 70% of the species recorded in catches were “rare” in subsamples (less than one individual per 10 kg subsample or less than one in every 389 individuals). A matrix was used to show the increase in the total number of species that were recorded in each catch as the percentage of the sorted catch increased. Simulation modelling showed that sorting small subsamples (about 10% of catch weights) identified about 50% of the total number of species caught in a trawl. Larger subsamples (50% of catch weight on average) identified about 80% of the total species caught in a trawl. The accuracy of estimating the abundance of each species also increased with increasing subsample size. For the “rare” species, sampling error was around 80% after sorting 10% of catch weight and was just less than 50% after 40% of catch weight had been sorted. For the “abundant” species (five or more individuals per 10 kg subsample or five or more in every 389 individuals), sampling error was around 25% after sorting 10% of catch weight, but was reduced to around 10% after 40% of catch weight had been sorted.