Catch estimation in the presence of declining catch rate due to gear saturation

One strategy for estimating total catch is to employ two separate surveys that independently estimate total fishing effort and catch rate with the estimator for total catch formed by their product. Survey designs for estimating catch rate often involve interviewing the fishermen during their fishing episodes. Such roving designs result in incomplete episode data and characteristically have employed a model in which the catch rate is assumed to be constant over time. This article extends the problem to that of estimating total catch in the presence of a declining catch rate due, e.g., to gear saturation. Using a gill net fishery as an example, a mean-of-ratios type of estimator for the catch rate together with its variance estimator are developed. Their performance is examined using simulations, with special attention given to effects of restrictions on the roving survey window. Finally, data from a Fraser River gill net fishery are used to illustrate the use of the proposed estimator and to compare results with those from an estimator based on a constant catch rate.     

Estimating species richness and catch per unit effort from boat electro‐fishing in a lowland river in temperate Australia

Abstract Biodiversity estimates are typically a function of sampling effort and in this regard it is important to develop an understanding of taxon‐specific sampling requirements. Northern hemisphere studies have shown that estimates of riverine fish diversity are related to sampling effort, but such studies are lacking in the southern hemisphere. We used a dataset obtained from boat electro‐fishing the fish community along an essentially continuous 13‐km reach of the Murrumbidgee River, Australia, to investigate sampling effort effects on fish diversity estimates. This represents the first attempt to investigate relationships between sampling effort and the detection of fish species in a large lowland river in Australia. Seven species were recorded. Species‐specific patterns in catch per unit effort were evident and are discussed in terms of solitary and gregarious species, recreational fishing and the monitoring of rare and threatened species. There was a requirement to sample substantial lengths of river to describe total species richness of the fish community in this river reach. To this end, randomly allocated sampling effort and use of species richness estimators produced accurate estimates of species richness without the requirement for excessive levels of effort. Twenty operations were required to estimate species richness at this site, highlighting the need for comparable studies of river fish communities in lowland rivers elsewhere in Australia and the southern hemisphere.     

Estimation of fish consumption rates based on a creel angler survey of an urban river in New Jersey,USA

Abstract

A one-year angler intercept survey was conducted on the lower 17 miles of the Passaic River, an urban industrialized river that flows through Newark, New Jersey. The purpose of the survey was to collect data about anglers’ behaviors and fish consumption habits in order to calculate exposure factors for a human health risk assessment of the Study Area. This paper focuses on estimating site-specific fish consumption rates for LPRSA anglers that consume their catch. The study design included on-site interviews and counts (angler enumeration). Forty survey locations were included in the stratified random sampling plan; interviews were conducted on 136 days and counts on 164 days. After matching intercepts with the same angler, a total of 294 anglers were interviewed, of which 25 reported consuming their catch. LPRSA fishing trips ranged from 2 to nearly 50 annual trips for anglers who reported consuming their catch. Species caught and reported to be consumed included carp, catfish, white perch, smallmouth bass, and eel. The estimated mean and 90th percentile consumption rates for the population of consuming anglers are 5.0 and 8.8?g/day, respectively. Based on sensitivity analyses, the 90th percentile fish consumption rates range from approximately 4 to 18?g/day.     

Adaptive Cluster Sampling in the Context of Restoration

Abundance is an important population state variable for monitoring restoration progress. Efficient sampling often proves difficult, however, when populations are sparse and patchily distributed, such as early after restoration planting. Adaptive cluster sampling (ACS) can help by concentrating search effort in high density areas, improving the encounter rate and the ability to detect a population change over time. To illustrate the problem, I determined conventional design sample sizes for estimating abundance of 12 natural populations and 24 recently planted populations (divided among two preserves) of Lupinus perennis L. (wild blue lupine). I then determined the variance efficiency of ACS relative to simple random sampling at fixed effort and cost for 10 additional planted populations in two habitats (field vs. shrubland). Conventional design sample sizes to estimate lupine stem density with 10% or 20% margins of error were many times greater than initial sample size and would require sampling at least 90% of the study area. Differences in effort requirements were negligible for the two preserves and natural versus planted populations. At fixed sample size, ACS equaled or outperformed simple random sampling in 40% of populations; this shifted to 50% after correcting for travel time among sample units. ACS appeared to be a better strategy for inter‐seeded shrubland habitat than for planted field habitat. Restoration monitoring programs should consider adaptive sampling designs, especially when reliable abundance estimation under conventional designs proves elusive.     

Estimation of overall fishing intensity of tuna longline fishery-yellowfin tuna (Thunnus albacares) fishery in the indian ocean as a case study

According to the FAO catch statistics, the total catch of yellowfin tuna (Thunnus albacares) from the Indian Ocean is characterised by decline in the longline fishery and rapid increase in the surface fishery. In the present communication, an attempt has been made to estimate the overall effective fishing intensity of longline fishery for yellowfin tuna by the Japanese longliners during the years 1973–1975. The results on areas and seasons of effective effort expended are presented, along with estimates of tuna availability, effective fishing intensity and the relative gear efficiency.     

The Influence of Mark-Recapture Sampling Effort on Estimates of Rock Lobster Survival

Five annual capture-mark-recapture surveys on Jasus edwardsii were used to evaluate the effect of sample size and fishing effort on the precision of estimated survival probability. Datasets of different numbers of individual lobsters (ranging from 200 to 1,000 lobsters) were created by random subsampling from each annual survey. This process of random subsampling was also used to create 12 datasets of different levels of effort based on three levels of the number of traps (15, 30 and 50 traps per day) and four levels of the number of sampling-days (2, 4, 6 and 7 days). The most parsimonious Cormack-Jolly-Seber (CJS) model for estimating survival probability shifted from a constant model towards sex-dependent models with increasing sample size and effort. A sample of 500 lobsters or 50 traps used on four consecutive sampling-days was required for obtaining precise survival estimations for males and females, separately. Reduced sampling effort of 30 traps over four sampling days was sufficient if a survival estimate for both sexes combined was sufficient for management of the fishery.     

Total catch of a red-listed marine species is an order of magnitude higher than official data

Accurate information on total catch and effort is essential for successful fisheries management. Officially reported landings, however, may be underestimates of total catch in many fisheries. We investigated the fishery for the nationally red-listed European lobster (Homarus gammarus) in south-eastern Norway. Probability-based strip transect surveys were used to count buoys in the study area in combination with catch per unit effort data obtained independently from volunteer catch diaries, phone interviews, and questionnaires. We estimate that recreational catch accounts for 65% of total catch in the study area. Moreover, our results indicate that only a small proportion (24%) of lobsters landed commercially were sold through the legal market and documented. Total estimated lobster catch was nearly 14 times higher than reported officially. Our study highlights the need for adequate catch monitoring and data collection efforts in coastal areas, presents a clear warning to resource managers that illegal, unreported and unregulated (IUU) fisheries in coastal areas should not be ignored, and shows the potential impact of recreational fisheries.     

Characterizing fishing effort and spatial extent of coastal fisheries

Biodiverse coastal zones are often areas of intense fishing pressure due to the high relative density of fishing capacity in these nearshore regions. Although overcapacity is one of the central challenges to fisheries sustainability in coastal zones, accurate estimates of fishing pressure in coastal zones are limited, hampering the assessment of the direct and collateral impacts (e.g., habitat degradation, bycatch) of fishing. We compiled a comprehensive database of fishing effort metrics and the corresponding spatial limits of fisheries and used a spatial analysis program (FEET) to map fishing effort density (measured as boat-meters per km2) in the coastal zones of six ocean regions. We also considered the utility of a number of socioeconomic variables as indicators of fishing pressure at the national level; fishing density increased as a function of population size and decreased as a function of coastline length. Our mapping exercise points to intra and interregional 'hotspots' of coastal fishing pressure. The significant and intuitive relationships we found between fishing density and population size and coastline length may help with coarse regional characterizations of fishing pressure. However, spatially-delimited fishing effort data are needed to accurately map fishing hotspots, i.e., areas of intense fishing activity. We suggest that estimates of fishing effort, not just target catch or yield, serve as a necessary measure of fishing activity, which is a key link to evaluating sustainability and environmental impacts of coastal fisheries.     

Fish stock assessment in lakes based on mass removal

The efficiency of mass removal of fishes can potentially be assessed using catch statistics collected during intensive fishing periods. The calculations are based on the general assumption that a decline in population size will produce a decline in catch per unit effort (cpue). When the removal is efficient it is possible to estimate the population size both at the beginning of the fishing period and after the removal.
Two examples are based on the winter seine net fishing of vendace ( Coregonus albula ) in Lake Pyhäjärvi, SW Finland in (1) 1983–1984 and (2) 1989–1990. The effects of the error in catch composition samples and random changes in probabilities of capture during the removal period on the final estimates of initial stock sizes are also examined.
The precision of the population estimates shown here was greatly influenced by the violation of the underlying assumption that the probability of capture is equal for all members of the target age group. In any case, particular attention should be paid to optimizing the sampling programme so that it will reveal the best information on the exploited stock with the resources in hand.     

World whale stocks

The history of whaling is very largely one of repeated over-exploitation of the various whale stocks which became available through discovery or technological advance. Modern whaling has similarly caused considerable reductions in the numbers of some species in the major whaling grounds. Stock assessment methods are based on catch and effort statistics, biological information including age and reproductive status, marking and sightings records. Catch effort data have to be used with caution, because of changes in species preference, shifts in the whaling grounds and national fleet variations. With allowance made for these factors, cumulative catches adjusted for recruitment can be used to estimate the initial stock number. Changes in stock density after known catches also lead to abundance estimates. Logarithmic regression of age composition data are used to find the total mortality rates. The natural mortality can be estimated from early season catches in a fishery or pre-fishery year classes caught more recently; fishing mortality is found by subtraction, which again leads to abundance estimates. Mathematical approaches incorporating recruitment estimates from actual age composition data and theoretical population models have been employed. Additional estimates come from mark release-recapture experiments and direct sightings counts from whaling vessels and research ships. The latter are the only means of estimating the protected species. The yields which the various stocks can sustain are calculated from direct observations and theoretical considerations of the changes in recruitment, largely due to increased pregnancy rates and the lower ages at sexual maturity which occur in exploited stocks. The results of all the available analyses have been compared and combined to produce the population estimates and yields tabulated. The object of whale management is to bring all stocks to the levels providing the maximum or optimum sustainable yields. These are defined in terms of numbers at the moment, but may be expressed as biomass in the future.     

Exploring Spatiotemporal Trends in Commercial Fishing Effort of an Abalone Fishing Zone: A GIS-Based Hotspot Model

Assessing patterns of fisheries activity at a scale related to resource exploitation has received particular attention in recent times. However, acquiring data about the distribution and spatiotemporal allocation of catch and fishing effort in small scale benthic fisheries remains challenging. Here, we used GIS-based spatio-statistical models to investigate the footprint of commercial diving events on blacklip abalone (Haliotis rubra) stocks along the south-west coast of Victoria, Australia from 2008 to 2011. Using abalone catch data matched with GPS location we found catch per unit of fishing effort (CPUE) was not uniformly spatially and temporally distributed across the study area. Spatial autocorrelation and hotspot analysis revealed significant spatiotemporal clusters of CPUE (with distance thresholds of 100’s of meters) among years, indicating the presence of CPUE hotspots focused on specific reefs. Cumulative hotspot maps indicated that certain reef complexes were consistently targeted across years but with varying intensity, however often a relatively small proportion of the full reef extent was targeted. Integrating CPUE with remotely-sensed light detection and ranging (LiDAR) derived bathymetry data using generalized additive mixed model corroborated that fishing pressure primarily coincided with shallow, rugose and complex components of reef structures. This study demonstrates that a geospatial approach is efficient in detecting patterns and trends in commercial fishing effort and its association with seafloor characteristics.     

Using marine reserves to estimate fishing mortality

The proportion of a fish stock that is killed by fishing activity is often calculated as the catch divided by the estimated stock biomass. However, stock biomass is notoriously difficult to estimate reliably, and moreover, the catch may be uncertain or misreported and does not include losses due to discarding. In all too many fisheries, these difficulties have lead to underestimates of total fishing mortality and the commercial demise of the fishery. No‐take marine reserves eliminate fishing mortality from within their boundaries and, for species that exhibit seasonal migratory behaviour, comparison of reserves with fished areas can provide direct estimates of the proportion killed by fishing. For an important exploited species in New Zealand, seasonal changes in density of sub‐legal fish at three marine reserves were similar in both reserve and adjacent non‐reserve areas. However, this result did not hold for legal‐size fish, and the difference in seasonal change between reserved and non‐reserved areas was used to obtain direct estimates of the total localized fishing mortality in the non‐reserve area over 6‐month periods. Estimates of the percentage of legal‐size fish killed by fishing ranged from 70 to 96%. These results demonstrate an unanticipated practical benefit from marine reserves that goes beyond their ecological role.     

Estimating population density from indirect sign: track counts and the Formozov–Malyshev–Pereleshin formula

For many purposes it is often desirable to estimate animal population densities over large areas. Where total counts are not possible and sightings are relatively rare, a range of methods exists to estimate densities from indirect sign. Such methods are frequently unreliable and usually require independent calibration or confirmation. We present an analytical method for estimating population density from track counts. The method, widely known in the Russian Federation but not in the English language scientific literature, requires counts of tracks of known age, together with estimates of animal daily travel distances. We use simulations to verify the theoretical basis of the approach and to indicate potential precision that may be achieved. We illustrate application of the approach using a large data set on ungulate track counts in the Russian Far East. We suggest that under most circumstances, nonparametric bootstrapping will be the most appropriate method for deriving estimates of confidence intervals about density estimates. As with other approaches to estimating density from indirect sign, the method that we discuss is vulnerable to violations of an array of underlying assumptions. However, it is easily applied and could represent an important method by which the relationship between indices of abundance and absolute density can be understood.     

On the estimation of species richness based on the accumulation of previously unrecorded species

Estimation of species richness of local communities has become an important topic in community ecology and monitoring. Investigators can seldom enumerate all the species present in the area of interest during sampling sessions. If the location of interest is sampled repeatedly within a short time period, the number of new species recorded is typically largest in the initial sample and decreases as sampling proceeds, but new species may be detected if sampling sessions are added. The question is how to estimate the total number of species. The data collected by sampling the area of interest repeatedly can be used to build species accumulation curves: the cumulative number of species recorded as a function of the number of sampling sessions (which we refer to as “species accumulation data”). A classic approach used to compute total species richness is to fit curves to the data on species accumulation with sampling effort. This approach does not rest on direct estimation of the probability of detecting species during sampling sessions and has no underlying basis regarding the sampling process that gave rise to the data. Here we recommend a probabilistic, nonparametric estimator for species richness for use with species accumulation data. We use estimators of population size that were developed for capture‐recapture data, but that can be used to estimate the size of species assemblages using species accumulation data. Models of detection probability account for the underlying sampling process. They permit variation in detection probability among species. We illustrate this approach using data from the North American Breeding Bird Survey (BBS). We describe other situations where species accumulation data are collected under different designs (e.g., over longer periods of time, or over spatial replicates) and that lend themselves to of use capture‐recapture models for estimating the size of the community of interest. We discuss the assumptions and interpretations corresponding to each situation.     

Efficacy of novel sampling approaches for surveying specialised recreational fisheries

Advances in fishing technologies have increased the efficiency and diversification of recreational fisheries. This poses challenges for surveying specialised or ‘hard-to-reach’ recreational fishers (e.g. sport fishers) that may take the majority of the recreational catch for some species, but are too rare within the general population to be sampled cost-effectively using existing methods. We trialled two new methods—time-location sampling (TLS) and online diaries—for surveying specialised recreational longtail tuna (Thunnus tonggol) fishers. Results were compared with a concurrent traditional access point survey (APS). Online diaries were inexpensive but unsuitable for collecting representative data due to avidity, volunteerism, and differential recruitment bias. APS yielded high resolution data on catch, effort and size composition but was expensive and ineffective for sampling all components of the fishery. In contrast, TLS conducted at fishing tackle stores was cost-effective for accessing the breadth of fisher types due to the need for all fishers to purchase or to inspect fishing-related products at some point. Given the frequent absence of complete list frames for recreational fisheries, we suggest undertaking multiple TLS surveys to collect catch rate data and to simultaneously estimate population size using capture-recapture approaches in order to estimate the total recreational catch of species of interest.     

The artisanal fishery of Metangula,Lake Malawi/Niassa,East Africa

The artisanal fishery of Metangula, on the remote Mozambique coast of Lake Malawi/Niassa, was studied for four months during the rainy season from December 1998 to March 1999. Catch, catch composition and fishing effort were determined for the most important gear types. The different gears exploited different stocks of fish. Copadichromis spp. were caught mostly in deep-water seine (chirimila) nets and accounted for >50% of the total catch. Oreochromis spp. were the major catch in the beach seine fishery. Labeo mesops and Opsaridium microcephalus were the most important catches in gill nets. The catfishes Bathyclarias spp. and Bagrus meridionalis dominated the long line fishery. Since 1983 there has been a considerable increase in fishing effort. The number of beach seines, chirimila nets and long lines increased 2.8 times, 2.9 times and 2.5 times, respectively, and there was a fourfold increase in the number of gear owners and fishing assistants. Comparison with data for Chembe Village in Malawi indicates that the fish populations at Metangula are not yet as impacted as they are on the populous and accessible Malawi coast. Some constraints on the fishery include the lack of management and financial support, poor gear and infrastructure, and the lack of access to markets.     

A parametric model for estimating prevalence,incidence, and mean bout duration from point sampling

Point or instantaneous sampling refers to the scoring of presence or absence of behavior at the end of equally spaced intervals of time and is used to estimate prevalence. The literature cited demonstrates that point sampling does not adequately estimate frequency or mean bout duration. A parametric model is developed based on exponentially distributed times of behavior and intervening nonbehavior, thus enabling estimators of mean bout length and incidence. Variance estimators are provided and a method is suggested for designing sample situations which control the variance of the prevalence estimator. The paper concludes the theoretical investigation with a thorough Monte Carlo investigation and application to a “real-life” problem. The point sample estimators compare favorably with continuous observation under appropriate choice of sampling interval and under approximately exponential assumptions.     

Inverse Adaptive Cluster Sampling

Consider a population in which the variable of interest tends to be at or near zero for many of the population units but a subgroup exhibits values distinctly different from zero. Such a population can be described as rare in the sense that the proportion of elements having nonzero values is very small. Obtaining an estimate of a population parameter such as the mean or total that is nonzero is difficult under classical fixed sample-size designs since there is a reasonable probability that a fixed sample size will yield all zeroes. We consider inverse sampling designs that use stopping rules based on the number of rare units observed in the sample. We look at two stopping rules in detail and derive unbiased estimators of the population total. The estimators do not rely on knowing what proportion of the population exhibit the rare trait but instead use an estimated value. Hence, the estimators are similar to those developed for poststratification sampling designs. We also incorporate adaptive cluster sampling into the sampling design to allow for the case where the rare elements tend to cluster within the population in some manner. The formulas for the variances of the estimators do not allow direct analytic comparison of the efficiency of the various designs and stopping rules, so we provide the results of a small simulation study to obtain some insight into the differences among the stopping rules and sampling approaches. The results indicate that a modified stopping rule that incorporates an adaptive sampling component and utilizes an initial random sample of fixed size is the best in the sense of having the smallest variance.     

On the Optimal Size of Marine Reserves

The excessive and unsustainable exploitation of our marine resources has led to the promotion of marine reserves as a fisheries management tool. Marine reserves, areas in which fishing is restricted or prohibited, can offer opportunities for the recovery of exploited stock and fishery enhancement. This study examines the impact of the creation of marine protected areas, from both economic and biological perspectives. The consequences of reserve establishment on the long-run equilibrium fish biomass and fishery catch levels are evaluated. We include reserve size as control variable to maximize catch at equilibrium. A continuous time model is used to simulate the effects of reserve size on fishing catch. Fish movements between the sites is assumed to take place at a faster time scale than the variation of the stock and the change of the fleet size. We take advantage of these two time scales to derive a reduced model governing the dynamics of the total fish stock and the fishing effort. Simulation results suggest that the establishment of a protected marine reserve will always lead to an increase in total fish biomass, an optimal size of a marine reserve can achieve to maximize the catch at equilibrium.     

The state of exploitation of the commercial demersal fishery of Cameroon

The state of exploitation of the demersal fisheries resources of Cameroon has been assessed using the classic Schaefer's (1954) model and the Gulland'ss (1961) moving average. The euilibrium yield found with the Schaefer method is statistically different (95 % probability) and higher than the Gulland approach. Because equilibrium models consistently over-estimate MSY and its related optimum effort, management option should target the 95 % value of the estimated parameters. The resources are being over-fished; as an immediate alternative to the urgent concern, catch and/or effort quotas could be allocated to the various fishing companies, with the total allocated catch and/or effort (for all fishing companies) 5% less than the estimated parameters. Enforcement control of that policy would be simplified as fishing activities are localised in the two main estuaries of the “Cameroon River” and Riodel-Rey;results should be complemented by economic studies of the fishery, as these economic factors would explain or better predict the behaviour of the fishing industry.