Cormorant Catch Concerns for Fishers: Estimating the Size-Selectivity of a Piscivorous Bird

Conflict arises in fisheries worldwide when piscivorous birds target fish species of commercial value. This paper presents a method for estimating size selectivity functions for piscivores and uses it to compare predation selectivities of Great Cormorants (Phalacrocorax carbo sinensis L. 1758) with that of gill-net fishing on a European perch (Perca fluviatilis L. 1758) population in the Curonian Lagoon, Lithuania. Fishers often regard cormorants as an unwanted “satellite species”, but the degree of direct competition and overlap in size-specific selectivity between fishers and cormorants is unknown. This study showed negligible overlap in selectivity between Great Cormorants and legal-sized commercial nets. The selectivity estimation method has general application potential for use in conjunction with population dynamics models to assess fish population responses to size-selective fishing from a wide range of piscivorous predators.     

Inland fisheries development versus aquatic biodiversity conservation in China and its global implications

Reviews in Fish Biology and Fisheries - China is unique among nations on account of its rich aquatic biodiversity (1443 inland fish species comprising 10% of those worldwide), status as the...     

Do young on‐grown eels,Anguilla anguilla (Linnaeus, 1758), outperform glass eels after transition to a natural prey diet?

Survival rates among European eels, Anguilla anguilla (Linnaeus, 1758), on‐grown using a formulated diet in a commercial aquaculture facility, were compared with glass eels from the same cohort following their transition to a natural prey diet in the laboratory. Treatments included zero, 42‐day, and 196‐day periods of grow‐out prior to 30‐day experimental periods when eels were fed Chironomus spp. larvae (10 tanks, each containing 240‐L water and 40 glass or 10 on‐growing eels; 12:12 hr photoperiod; water temperature 18°C). All glass eels survived, compared to 87% (42‐day) and 99% (196‐day) for on‐grown eels. Although the eels on‐grown for 196 days had a high survival rate, they did lose weight. Farm‐reared eels may have accumulated sufficient resources over the 196‐days to survive the first 30 days after weaning from a formulated diet, but not for an additional 30 days (84% survival). Lack of superior survival rates among on‐grown eels challenges the presumed benefits of releasing on‐grown eels for population restoration.     

Silver eel,Anguilla anguilla (Linnaeus, 1758), migration patterns in lowland rivers and lagoons in the North‐Eastern region of their distribution range

Escapement success and migration patterns of silver eels Anguilla anguilla (L.) was studied by acoustic telemetry in three natural free‐flowing and one dammed river and in Curonian Lagoon in Lithuania. Mean downstream migration speed and escapement success were almost the same in the shorter 210 km dammed river (52%, 13.6 km/day) and the considerably longer 300–480 km free‐flowing rivers (53%, 10.7 km/day). Despite the similarity between migration speed in the Curonian Lagoon (14.6 km/day) to that in rivers, migration success was significantly higher (71%) in the Lagoon. Although a majority of silver eels in Lithuania start migrating downstream in spring, the peak of eel migration into the Baltic Sea was observed during late fall. Overall migration success in the rivers and the Lagoon was 35%. Relatively low escapement may have negative consequences on the success on eel stock restoration and must be addressed when strategically planning for the production of spawners.     

Frailty‐Based Competing Risks Model for Multivariate Survival Data

Summary In this work, we provide a new class of frailty‐based competing risks models for clustered failure times data. This class is based on expanding the competing risks model of Prentice et al. (1978, Biometrics 34 , 541–554) to incorporate frailty variates, with the use of cause‐specific proportional hazards frailty models for all the causes. Parametric and nonparametric maximum likelihood estimators are proposed. The main advantages of the proposed class of models, in contrast to the existing models, are: (1) the inclusion of covariates; (2) the flexible structure of the dependency among the various types of failure times within a cluster; and (3) the unspecified within‐subject dependency structure. The proposed estimation procedures produce the most efficient parametric and semiparametric estimators and are easy to implement. Simulation studies show that the proposed methods perform very well in practical situations.     

Using species distribution models to assess the long-term impacts of changing oceanographic conditions on abalone density in south east Australia

Warming from climate change and resulting increases in energy stored in the oceans is causing changes in the hydrodynamics and biogeochemistry of marine systems, exacerbating current challenges facing marine fisheries. Although studies have evaluated effects of rising temperatures on marine species, few have looked at these impacts along with other environmental drivers over long time periods. In this study, we associate long-term density of blacklip abalone to changing oceanographic conditions in a climate change ‘hot-spot’ off southeast Australia. We downscaled and hind-casted existing hydrodynamic models to provide information on waves and currents over 25 yr and used this information to run biophysical connectivity models. We combined the connectivity models with 21 yr of data on abalone density, temperature, seafloor habitat, and the effects of a disease outbreak in an machine learning modeling approach to develop a spatio-temporal model of abalone density. We found that the combination of temperature, connectivity, current speed, wave orbital velocity, fishery catch, depth, reef structure and a disease outbreak explain 70% of variation in abalone density and allowed us to create 30 m resolution predictive grids with 75% accuracy. An emerging hotspot analysis run on the individual predictive grids from each year detected a predominance of low-density grids across the region, with 49.5% of cells classified as cold spots, 14.3% as hotspots and 36.2% with no significant patterns observed. This type of spatio-temporal analysis provides important insights into how changing environmental conditions are impacting density in an important fishery species, allowing for better adaptive management in the face of future climate change.     

Semiparametric estimation of marginal hazard function from case-control family studies

Estimating marginal hazard function from the correlated failure time data arising from case-control family studies is complicated by noncohort study design and risk heterogeneity due to unmeasured, shared risk factors among the family members. Accounting for both factors in this article, we propose a two-stage estimation procedure. At the first stage, we estimate the dependence parameter in the distribution for the risk heterogeneity without obtaining the marginal distribution first or simultaneously. Assuming that the dependence parameter is known, at the second stage we estimate the marginal hazard function by iterating between estimation of the risk heterogeneity (frailty) for each family and maximization of the partial likelihood function with an offset to account for the risk heterogeneity. We also propose an iterative procedure to improve the efficiency of the dependence parameter estimate. The simulation study shows that both methods perform well under finite sample sizes. We illustrate the method with a case-control family study of early onset breast cancer.     

Multivariate survival analysis for case-control family data

Multivariate survival data arise from case-control family studies in which the ages at disease onset for family members may be correlated. In this paper, we consider a multivariate survival model with the marginal hazard function following the proportional hazards model. We use a frailty-based approach in the spirit of Glidden and Self (1999) to account for the correlation of ages at onset among family members. Specifically, we first estimate the baseline hazard function nonparametrically by the innovation theorem, and then obtain maximum pseudolikelihood estimators for the regression and correlation parameters plugging in the baseline hazard function estimator. We establish a connection with a previously proposed generalized estimating equation-based approach. Simulation studies and an analysis of case-control family data of breast cancer illustrate the methodology's practical utility.