<Emphasis Type="Italic">Noctiluca</Emphasis> and copepods grazing on the phytoplankton community in a nutrient-enriched coastal environment along the southwest coast of India

The relative grazing impact of Noctiluca scintillans (hereafter referred only Noctiluca) and copepods (Acrocalanus gracilis, Paracalanus parvus, Acartia danae and Oithona similis) on the phytoplankton community in an upwelling–mudbank environment along the southwest coast India is presented here. This study was carried out during the Pre-Southwest Monsoon (April–May) to the Late Southwest Monsoon (August) period in 2014. During the sampling period, large hydrographical transformation was evident in the study area (off Alappuzha, Southwest coast of India); warmer Pre-Southwest Monsoon water column condition got transformed into cooler and nitrate-rich hypoxic waters during the Southwest Monsoon (June–August) due to intense coastal upwelling. Copepods were present in the study area throughout the sampling period with a noticeable increase in their abundance during the Southwest Monsoon. On the other hand, the first appearance of Noctiluca in the sampling location was during the Early Southwest Monsoon (mid-June) and thereafter their abundance increased towards the Peak Southwest Monsoon. The grazing experiments carried out as per the food removal method showed noticeable differences in the feeding preferences of Noctiluca and copepods, especially on the different size fractions of phytoplankton. Noctiluca showed the highest positive electivity for the phytoplankton micro-fraction (av. 0.49 ± 0.04), followed by nano-fraction (av. 0.17 ± 0.04) and a negative electivity for the pico-fraction (av. ?0.66 ± 0.06). In total ingestion of Noctiluca, micro-fraction contribution (83.7%) was significantly higher compared to the nano- (15.7%) and pico-fractions (0.58%). On the other hand, copepods showed the highest positive electivity for the phytoplankton nano-fraction (av. 0.38 ± 0.04) followed by micro- (av. -0.17 ± 0.05) and pico-fractions (av. ?0.35 ± 0.05). Similarly, in total ingestion of copepods, nano-fraction (69.7%) was the highest followed by micro- (28.9%) and pico-fractions (1.37%). The grazing pressure of Noctiluca on the total phytoplankton was found to be 27.7% of the standing stock and 45.6% of the production, whereas in the case of copepods, it was 9.95% of the standing stock and 16.6% of the production. The study showed that the grazing pressure of Noctiluca on the total phytoplankton as well as larger phytoplankton fraction was 2.8- and 8-folds higher than that of the copepods. This suggests the leading role of Noctiluca as an effective grazer of larger phytoplankton along the southwest west coast of India, especially during the Peak/Late Southwest Monsoon.     

A moving target—incorporating knowledge of the spatial ecology of fish into the assessment and management of freshwater fish populations

Freshwater fish move vertically and horizontally through the aquatic landscape for a variety of reasons, such as to find and exploit patchy resources or to locate essential habitats (e.g., for spawning). Inherent challenges exist with the assessment of fish populations because they are moving targets. We submit that quantifying and describing the spatial ecology of fish and their habitat is an important component of freshwater fishery assessment and management. With a growing number of tools available for studying the spatial ecology of fishes (e.g., telemetry, population genetics, hydroacoustics, otolith microchemistry, stable isotope analysis), new knowledge can now be generated and incorporated into biological assessment and fishery management. For example, knowing when, where, and how to deploy assessment gears is essential to inform, refine, or calibrate assessment protocols. Such information is also useful for quantifying or avoiding bycatch of imperiled species. Knowledge of habitat connectivity and usage can identify critically important migration corridors and habitats and can be used to improve our understanding of variables that influence spatial structuring of fish populations. Similarly, demographic processes are partly driven by the behavior of fish and mediated by environmental drivers. Information on these processes is critical to the development and application of realistic population dynamics models. Collectively, biological assessment, when informed by knowledge of spatial ecology, can provide managers with the ability to understand how and when fish and their habitats may be exposed to different threats. Naturally, this knowledge helps to better evaluate or develop strategies to protect the long-term viability of fishery production. Failure to understand the spatial ecology of fishes and to incorporate spatiotemporal data can bias population assessments and forecasts and potentially lead to ineffective or counterproductive management actions.     

Advantages of Geographically Weighted Regression for Modeling Benthic Substrate in Two Greater Yellowstone Ecosystem Streams

Stream habitat assessments are commonplace in fish management, and often involve nonspatial analysis methods for quantifying or predicting habitat, such as ordinary least squares regression (OLS). Spatial relationships, however, often exist among stream habitat variables. For example, water depth, water velocity, and benthic substrate sizes within streams are often spatially correlated and may exhibit spatial nonstationarity or inconsistency in geographic space. Thus, analysis methods should address spatial relationships within habitat datasets. In this study, OLS and a recently developed method, geographically weighted regression (GWR), were used to model benthic substrate from water depth and water velocity data at two stream sites within the Greater Yellowstone Ecosystem. For data collection, each site was represented by a grid of 0.1 m² cells, where actual values of water depth, water velocity, and benthic substrate class were measured for each cell. Accuracies of regressed substrate class data by OLS and GWR methods were calculated by comparing maps, parameter estimates, and determination coefficient r ². For analysis of data from both sites, Akaike’s Information Criterion corrected for sample size indicated the best approximating model for the data resulted from GWR and not from OLS. Adjusted r ² values also supported GWR as a better approach than OLS for prediction of substrate. This study supports GWR (a spatial analysis approach) over nonspatial OLS methods for prediction of habitat for stream habitat assessments.