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\u27s 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

Fishery independent survey datasets of abalone populations on subtidal coastal reefs in southeastern Australia

Assessing the status or exploited marine fish populations often relies on fishery dependent catch and effort data reported by licensed commercial fishers in compliance with regulations and by recreational anglers voluntarily. This invariably leads to bias towards the fraction of a fish population or community that can be legally fished i.e., the stock as defined by legal minimum lengths and spatial boundaries. Data are restricted to populations which continue to be exploited at the expense of obtaining data on previously exploited and unexploited populations [1,2], so if a fishery is contracting spatially over time, then successively less of the overall fish community is monitored with bias towards where biomass is highest or most accessible [3]. A viable alternative is to conduct population monitoring surveys independently of a fishery to obtain information that is more broadly representative of the abundance, composition and size structure of fish communities and their supporting habitats [4–6]. Whereas catch and effort data often must be de-identified and aggregated to protect the confidentiality of fishers’ commercial and personal interests, this constraint does not exist for independently acquired monitoring data, collected at public expense and hence publicly available at high levels of spatial and temporal resolution. Time series underpins the utility of fishery independent survey (FIS) datasets in terms of the life histories of exploited fish species and the time frames of their responses to various combinations of fishing mortality and environmental fluctuations and trends [7].One-off surveys can establish a baseline and spatial distribution pattern, but regular surveys conducted consistently over time are necessary to detect trends from which population status can be inferred. We present several unique datasets focused on the commercially valuable blacklip abalone (Haliotis rubra), spanning three decades of annually collected data from up to 204 locations on subtidal rocky reefs along a coastline of almost 2500 km, the State of Victoria, Australia. It is rare for data to be collected consistently at this intensity over such a long period of monitoring [2], especially with surveys conducted by small teams of highly skilled research divers, some of whom up until recently had participated in every year.The data comprises ∼28,000 records from ∼4500 site surveys conducted during 1992 to 2021 [2]. Although the fixed site design remained unchanged, the number of sites surveyed varied over time, mostly increasing in number periodically, and the survey method was refined on several occasions. We defined three different variants in the survey method due to technological advancement for both enumerating abalone abundance and measuring shell size structure [7]. The relative abundance counts were standardized using a Bayesian generalized linear mixed model (GLMM) to test for interannual trends whilst allowing for inherent differences among sites, research divers, and their interactions [8]

Contrasting stock status trends obtained from survey and fishery CPUE, taking Larimichthys polyactis in Yellow Sea Large Marine Ecosystem as an example

Biological conservation of exploited fish species involves characterizing key aspects of their population dynamics using models as tools to estimate their biomass. The Bayesian state-space surplus production model in the open-source stock assessment tool Just Another Bayesian Biomass Assessment (JABBA) was used to assess small yellow croaker stock (Larimichthys polyactis) in the Yellow Sea large marine ecosystem (YSLME). In this study, Catch and the scientific survey catch per unit effort (CPUE) data of the overwintering grounds of small yellow croaker from 1985 to 2020 and three fishery CPUE data series (one original and two reconstructed) from the Chinese Fishery Statistical Yearbook were used to fit JABBA, respectively. The results showed that the trends in biomass obtained from the survey CPUE and from the fishery CPUE contrasted sharply. The independent survey CPUE-based JABBA showed a 54.4 % probability that the current resource status is over exploited (0.65 and 1.06 for B2020/BMSY and F2020/FMSY, respectively), whereas the fishery CPUE-based JABBA showed that the resource is in a healthy or recovering state (0.88–1.32 and 0.40–0.70 for B2020/BMSY and F2020/FMSY, respectively), and the estimates of the resource are overly optimistic. This discrepancy in biomass assessment arises because the fishing effort time series does not adequately reflect the technological advances in fishing vessels and their equipment. Therefore, assessment based on survey CPUE (predicted total allowable catch, TAC = 150,000 tons) is the preferable, more precautionary approach for establishing management reference points and informing management decisions, and estimates generated from the fisheries CPUE-based JABBA model (TAC = 165,000–210,000 tons) should be treated with caution. We found that the two reconstructed fishery CPUEs are more likely to produce model results closer to that of the survey CPUE-based JABBA than the original fishery CPUE. Further review and research on the correction of fishing effort and fishery CPUE in the YSLME is recommended before this data series is used in assessments aimed at biological conservation

A dynamic energy budget model of Fenneropenaeus chinensis with applications for aquaculture and stock enhancement

Dynamic energy budget (DEB) theory provides a framework for quantifying metabolic processes and biological rates. DEB models have been widely applied to aquaculture species, but this type of model has great potential for application to fisheries for stock assessment and enhancement. The shrimp Fenneropenaeus chinensis, widely distributed along the coast of China and Korea, is the most important fisheries and aquaculture species in China. With the AmP method, DEB parameters were estimated for the population along the coast of China. The parameter estimation achieved an overall goodness of fit with MRE of 0.131 and SMSE of 0.178. In comparison with similar species, the values of a few main parameters are relatively high including reserve capacity (Em), somatic maintenance (ṗM) and allocation fraction to growth and somatic maintenance (κ). This may reflect an adaptation to variation of environmental conditions. The model can predict the physiological behaviours including respiration, ammonia excretion and feeding rates reasonably well. It shows overall capability to predict the growth and reproduction with acceptable confidence in three main geographic regions. There are clear differences between the female and male with much faster growth rate of the former. Validations of the model have shown that it can adequately predict growth of the shrimp in both its natural distribution waters and land-based culture systems. This study provides important information for further development of modeling tools which can contribute to estimating the carrying capacity for stock enhancement and optimizing production from integrated multi-trophic aquaculture

Angling counts: Harnessing the power of technological advances for recreational fishing surveys

As the popularity of recreational fishing gathers global momentum, so does the importance of knowing the number of active anglers and their spatial behaviour. Conventional counting methods, however, can be inaccurate and time-consuming. Here we present two novel methods to monitor recreational fishing applied in Kaunas water reservoir (ca 65 km2), Lithuania, comparing their performance to a conventional visual count. First, we employed a remotely piloted fixed wing drone which conducted 39 missions distributed over one year and compared its accuracy to conventional visual land or boat-based counts. With these data we developed a linear model to predict the annual number of anglers depending on weekday and ice conditions. Second, we used anonymous data from a popular GPS-enabled sonar device Deeper®, used by anglers to explore underwater landscapes and to find fish. The sonar usage probability was calibrated with angler observations from drones using Bayesian methods, demonstrating that at any given time ~2 % of anglers are using the sonar device during the open water season and ~15 % during the ice fishing season. The calibrated values were then used to estimate the total number of anglers, given the daily records of sonar usage in Kaunas water reservoir. The predicted annual number of anglers from both linear drone-based and Bayesian sonar-based methods gave similar results of 25 and 27 thousand anglers within the area during the period of day surveyed, which corresponded to nearly 110 thousand angling trips in the total reservoir area annually. Our study shows high potential of both drone and fish finder digital devices for assessing recreational fishing activities through space and time

Are Lithuanian eels fat enough to reach the spawning grounds?

Stocks of the European eel Anguilla anguilla have been in a steep decline since the 1980s. Stocking of water bodies with juvenile eels captured in the wild to establish or enhance local populations has been a common practise in Europe for many decades. However, the degree of contribution by stocked eels to natural spawning capacity is poorly known and extensively debated. There have been suggestions that eels derived from stocking are less likely to contribute to the spawning stock due to a lack of navigational capability and lower fitness related to insufficiency of energetic resources. Results of the current study indicated that eels translocated long distances from the point of capture and released into inland waters in Lithuania are successfully undergoing the silvering process. A proportion of 23.7% (N = 27) among all migrating eels were described to be at the yellow (SI, SFII or SFIII) eel stage and downstream movements of these eels should be attributed to local movements, rather than spawning migration; 76.3% were assigned to the silver eel stage. This study suggests that 36.8% (N = 32) of downstream migrating silver eels of stocked origin had accumulated sufficient energetic resources for spawning migration and gonadal development and should be able to traverse the 7900-km distance to the presumptive spawning grounds in the Sargasso Sea. The rest of migrating silver eels (63.2%, N = 55) had insufficient energetic resources; the average potential swimming range of these eels was estimated to be 6135 ± 683 km