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.     

An Approach to Reaeration Coefficient Modeling in Local Surface Water Quality Monitoring

Reaeration coefficient (k ₂) for River Atuwara, Ogun State, Nigeria was calculated from dissolved oxygen and biochemical oxygen demand data collected over period of 3 months covering the two prevailing climatic seasons in the country. Both the Akaike and Bayesian information criteria were used in the selection and analysis of ten models to identify the most suitable reaeration coefficient (k ₂) model for Atuwara River. Models that passed the confidence limit were subjected to model evaluation using measures of agreement between observed and predicted data such as percent bias, Nash–Sutcliffe efficiency, and root mean square observation standard deviation ratio. The used approach yield better results than empirical models developed for local conditions while it is also useful in conserving scarce resources.     

The distribution and extent of heavy metal accumulation in song sparrows along Arizona’s upper Santa Cruz River

Heavy metals are persistent environmental contaminants, and transport of metals into the environment poses a threat to ecosystems, as plants and wildlife are susceptible to long-term exposure, bioaccumulation, and potential toxicity. We investigated the distribution and cascading extent of heavy metal accumulation in southwestern song sparrows (Melospiza melodia fallax), a resident riparian bird species that occurs along the US/Mexico border in Arizona’s upper Santa Cruz River watershed. This study had three goals: (1) quantify the degree of heavy metal accumulation in sparrows and determine the distributional patterns among study sites, (2) compare concentrations of metals found in this study to those found in studies performed prior to a 2009 international wastewater facility upgrade, and (3) assess the condition of song sparrows among sites with differing potential levels of exposure. We examined five study sites along with a reference site that reflect different potential sources of contamination. Body mass residuals and leukocyte counts were used to assess sparrow condition. Birds at our study sites typically had higher metal concentrations than birds at the reference site. Copper, mercury, nickel, and selenium in song sparrows did exceed background levels, although most metals were below background concentrations determined from previous studies. Song sparrows generally showed lower heavy metal concentrations compared to studies conducted prior to the 2009 wastewater facility upgrade. We found no cascading effects as a result of metal exposure.     

Assessment of long-term wastewater irrigation impacts on the soil geochemical properties and the bioaccumulation of heavy metals to the agricultural products

An extensive field survey was employed for assessing the impacts of long-term wastewater irrigation of forage crops and orange orchards in three suburban agricultural areas in Cyprus (areas I, II, and III), as compared to rainfed agriculture, on the soil geochemical properties and the bioaccumulation of heavy metals (Zn, Ni, Mn, Cu, Co) to the agricultural products. Both ryegrass fields and orange orchards in areas I and II were continuously wastewater irrigated for 10 years, whereas clover fields in area III for 0.5, 4, and 8 years. The results revealed that wastewater reuse for irrigation caused a slight increase in soil salinity and Cl^? content in areas I and II, and a remarkable increase, having strong correlation with the period in which wastewater irrigation was practiced, in area III. Soil salinization in area III was due to the high electrical conductivity (EC) of the wastewater applied for irrigation, attributed to the influx of seawater to the sewage collection network in area III. In addition, the wastewater irrigation practice resulted in a slight decrease of the soil pH values in area III, while a subtle impact was identified regarding the CaCO₃, Fe, and heavy metal content in the three areas surveyed. The heavy metal content quantified in the forage plants’ above-ground parts was below the critical levels of phytotoxicity and the maximum acceptable concentration in dairy feed, whereas heavy metals quantified in orange fruit pulp were below the maximum permissible levels (MPLs). Heavy metal phytoavailability was confined due to soil properties (high pH and clay content), as evidenced by the calculated low transfer factor (TF).