Sampling Gear Research Articles

Using historical catch data to evaluate predicted changes in fish relative abundance in response to a warming climate

Using models to predict future changes in species distributions in response to projected climate change is a common tool to aid management and species conservation. However, the assumption underlying this approach, that ecological processes remain stationary through time, can be unreliable, and more empirical tests are needed to validate predictions of biotic outcomes of global change. The scarcity of reliable historical and long‐term datasets can make these tests difficult. Moreover, incorporating abundance and multiple sampling methods can improve model predictions and usability for management. Our study 1) provides insight into how well models can predict environmental change under a warming climate, 2) incorporates multiple sampling gears and abundance data in modeling to better capture changes in populations and 3) shows the value of historical datasets for improving predictive models of population change. We used contemporary (2003–2019) and historical (1936–1964) abundance datasets of the North American fish largemouth bass Micropterus salmoides in lakes across the state of Michigan, USA. We developed Bayesian hierarchical models that leverage the use of multiple gears in contemporary lake surveys to estimate the relative catchabilities of largemouth bass for each gear and hindcast the models to predict historical abundance. Our estimates of relative density change over time were correlated with temperature change over time; increasing surface water temperature led to increasing largemouth bass density. Hindcasting models to historical lake temperatures performed similarly in predicting historical density to models predicting contemporary density. Our results suggest that models built using spatial environmental gradients can reliably predict population changes through time. Understanding the sampling methods and the environmental context of observational datasets can help researchers test for potential sampling biases and identify confounding factors that will improve predictions of future impacts of environmental change.

Evaluating population trends of juvenile Atlantic Sturgeon at low abundance in a dynamic estuarine environment (Hudson River, New York)

AbstractEvaluating population trends in dynamic estuarine environments can be challenging, especially when survey data include a high percentage of zero observations. In fishery‐independent surveys, zeros that come from reduced susceptibility to sample gears and reduced availability of the population to the survey impact survey catchability and negatively bias relative abundance indices. A zero‐inflated negative binomial model was used to standardize a juvenile Atlantic Sturgeon (Acipenser oxyrinchus oxyrinchus) relative abundance index (Hudson River, New York) that included a high proportion (42%) of zero observations and intra‐ and interannually variable covariates. Reduced susceptibility was related to low water temperature, with the percentage of zeroes increasing rapidly below 7°C. Availability was influenced by temperature and distance to salt front, as catch rates increased with temperature and peaked in mesohaline waters ~27 km downstream of the predicted salt front. An alternative index suggested significant population growth (r = 0.15; p‐value = 0.007) occurred from 2004 to 2015. The zero‐inflated model helped better understand Hudson River juvenile Atlantic Sturgeon ecology and relative trends in abundance, to better inform future management and monitoring decisions along the Atlantic Coast.

Calibration of fish counts in video surveys: a case study from the Southeast Reef Fish Survey

Changes to sampling gears or vessels can influence the catchability or detectability of fish, leading to biased trends in abundance. Despite the widespread use of underwater video cameras to index fish abundance and the rapid advances in video technology, few studies have focused on calibrating data from different cameras used in underwater video surveys. We describe a side-by-side calibration study (N = 143 paired videos) undertaken in 2014 to account for a camera change in the Southeast Reef Fish Survey, a regional-scale, multi-species reef fish survey along the southeast United States Atlantic coast. Slope estimates from linear regression for the 16 species included in the analyses ranged from 0.21 to 0.98, with an overall mean of 0.57, suggesting that original cameras (Canon Vixia HF-S200) observed an average of 43% fewer fish than newer cameras (GoPro Hero 3+). Some reef fish species had limited calibration sample sizes, such that borrowing calibration information from related or unrelated species was justified in some cases. We also applied calibrations to 11-year video time series of relative abundance of scamp Mycteroperca phenax and red snapper Lutjanus campechanus (N = 13,072 videos), showing that calibrations were critical to separating changes in camera sightability from true changes in abundance. We recommend calibrating data from video cameras anytime changes occur, and pairing video cameras to the extent possible to control for the spatial and temporal variability inherent in fish populations and environmental conditions. Following these guidelines, researchers will be able to maintain the integrity of valuable long-term video datasets despite intentional or unavoidable changes to video cameras over time.

Ichthyoplankton metabarcoding: An efficient tool for early detection of invasive species establishment.

Detection of invasive species is critical for management but is often limited by challenges associated with capture, processing and identification of early life stages. DNA metabarcoding facilitates large-scale monitoring projects to detect establishment early. Here, we test the use of DNA metabarcoding to monitor invasive species by sequencing over 5000 fishes in bulk ichthyoplankton samples (larvae and eggs) from four rivers of ecological and cultural importance in southern Canada. We were successful in detecting species known from each river and three invasive species in two of the four rivers. This includes the first detection of early life-stage rudd in the Credit River. We evaluated whether sampling gear affected the detection of invasive species and estimates of species richness, and found that light traps outperform bongo nets in both cases. We also found that the primers used for the amplification of target sequences and the number of sequencing reads generated per sample affect the consistency of species detections. However, these factors have less impact on detections and species richness estimates than the number of samples collected and analysed. Our analyses also show that incomplete reference databases can result in incorrectly attributing DNA sequences to invasive species. Overall, we conclude that DNA metabarcoding is an efficient tool for monitoring the early establishment of invasive species by detecting evidence of reproduction but requires careful consideration of sampling design and the primers used to amplify, sequence and classify the diversity of native and potentially invasive species.

Biological and life-history parameters for Labeo rosae Steindachner, 1894 and Oreochromis mossambicus (Peters, 1852) from Flag Boshielo Dam, Olifants River, South Africa

Inland fisheries contribute towards food security for rural communities living close to large aquatic systems. However, sustainable inland fisheries rely on accurate biological information for the target species at the proposed fishery location. In this study, the biological information for Labeo rosae Steindachner, 1894 and Oreochromis mossambicus (Peters, 1852) were determined at Flag Boshielo Dam, Olifants River system, Limpopo province, as part of a larger study to evaluate a small-scale gill net fishery at this impoundment. Nine fish surveys were conducted from February 2016 to April 2017 using mixed sampling gears. Labeo rosae exhibited positive allometric growth (b > 3) while O. mossambicus displayed negative growth (b < 3). For L. rosae, the asymptotic length was 391 mm L ∞, and the length-at-50% sexual maturity 165.8 mm. For O. mossambicus, the asymptotic length was 420 mm L ∞, and the length-at-50% sexual maturity 166.5 mm. Both species population stocks showed signs of exploitation. Total mortality for L. rosae was 0.57 yr−1 with a fishing exploitation of 0.21 yr−1, while total mortality of 0.78 yr−1 and fishing exploitation of 0.50 yr–1 were estimated for O. mossambicus. The biological data collected, apart from the length–weight parameters for O. mossambicus, are suitable for conducting a fisheries assessment for these species at Flag Boshielo Dam.

A comparative study of three fishery methods for sampling the invasive topmouth gudgeon (Pseudorasbora parva) in ponds.

Invasive fish threaten ponds' ecological status and their ecosystem services, therefore obtaining a representative sample of fish community composition is fundamental to fishery management, research and nature conservation. Estimates of the size distribution, density and biomass of the topmouth gudgeon (Pseudorasbora parva) model species of invasive fish in three ponds were compared among three sampling methods: electrofishing, fish-trapping and throw-netting. The study illustrates that the invasive fish, P. parva, can be detected by all tested fishing methods, yet our results clearly showed that there are pronounced differences among methods in population characteristic estimates. Electrofishing and throw-netting gave biased information on the size distribution of P. parva. Fish-trapping and throw-netting gave reasonable P. parva density and biomass estimates, while electrofishing clearly underestimated it. All tested methods showed a body size increment of P. parva between summer and autumn sampling sessions, yet neither throw-netting nor electrofishing recorded an increment in its density. Our study showed that fish-trapping is the most reliable and affordable method to estimate invasive P. parva population characteristics in ponds despite more time-demanding sampling. The success depends on the mesh size of sampling gear, operator skill and habitat structure. The cost-effectiveness of the selected methods and the importance of invasive fish monitoring in ponds is discussed. The sampling gear must be considered carefully according to the aim of the monitoring.

Checklist of the fish fauna of the Munim River Basin, Maranhão, north-eastern Brazil.

The Maranhão State harbours great fish diversity, but some areas are still undersampled or little known, such as the Munim River Basin in the northeast of the State. This lack of knowledge is critical when considering anthropogenic impacts on riverine systems especially in the face of major habitat destruction. These pressing threats mean that a comprehensive understanding of diversity is critical and fish checklists extremely relevant. Therefore, the present study provides a checklist of the fish species found in the Munim River Basin, Maranhão State, north-eastern Brazil, based on collected specimens. A total of 123 species were recorded for the Munim River Basin, with only two non-native species, Oreochromisniloticus and Colossomamacropomum, showing that the fish assemblage has relatively high ecological integrity. In addition, 29 species could not be identified at the species level, indicating the presence of species that are probably new to science in the Basin. A predominance of species belonging to the fish orders Characiformes and Siluriformes, with Characidae being recovered as the most species-rich family (21 species) agrees with the general pattern for river basins in the Neotropical Region. The total fish diversity was estimated by extensive fieldwork, including several sampling gears, carried out in different seasons (dry and rainy) and exploring different environments with both daily and nocturnal sampling, from the Basin's source to its mouth. A total of 84 sites were sampled between 2010 and 2022, resulting in 12 years of fieldwork. Fish assemblages were distinct in the Estuary and Upper river basin sections and more similar in the Lower and Middle sections indicating environmental filtering processes. Species were weakly nested across basin sections, but unique species were found in each section (per Simpsons Index). High variability of species richness in the Middle river basin section is likely due to microhabitat heterogeneity supporting specialist fish communities.

Small Sample Gear Fault Diagnosis Method Based on Transfer Learning

Small Sample Gear Fault Diagnosis Method Based on Transfer Learning

A checklist of fish fauna of Shuklaphanta National Park, Kanchanpur, Nepal

An ichthyological survey along with a desk study was carried out at Shuklaphanta National Park, Kanchanpur, Nepal which harbours varieties of fish fauna. To this end, field surveys were held during the spring (March to April) and the autumn (September to October) in 2020 aiming to determine the fish checklist present in the study area. Cast net of different mesh sizes, scoop net, hook and line, flow modification, and local traps of bamboo baskets were the sampling gears employed to collect fish samples. Based on the present survey along with historical records, a combined fish checklist depicted a total of 88 fish species belonging to 7 orders, 21families and 49 genera which apart from 35 fish species adopted from the past studies carried out from 1973 to 2019, and 53 species were recorded during our field survey in 2020. This paper indicates the need for further exploration and studies and proper sustainable management for conserving important fish fauna from being overexploited.

Species Composition of Fish in Yedang Reservoir and Characteristics by Sampling Gears

Species Composition of Fish in Yedang Reservoir and Characteristics by Sampling Gears

Development of an adaptive quality control loop in micro-production using machine learning, analytical gear simulation, and inline focus variation metrology for zero defect manufacturing

This publication addresses the adaptive control of manufacturing deviations in micro gear hobbing. We aim to establish Zero Defect Manufacturing in a series production using manipulated parts with a small sample size and machine learning. Therefore, optical focus variation metrology is used to measure gears inline. Afterward, the evaluation of measurement results based on trained models and the transfer of correction parameters back to the machine tool through control algorithms are established. Critical parameters of the manufacturing process are identified through preliminary tests, which are varied using Latin Hypercube Sampling. The resulting experimental plan defines manipulated deviations for manufacturing 200 sample gears representing production variations. The evaluation according to parameter-based gear deviations enables the modeling of influencing quantities using machine learning. This information provides a control algorithm for the feedback of correction values to the machine tool based on data analyses. After validation, it is shown that the current state of measurement technology enables the inline quality control of micro-components. The final control loop achieved accuracies in the micrometer range at detection levels of over 90%. Consequently, these results form a basis for implementing future adaptive quality control loops within data-driven production.

Subtle fisheries gear model differences substantially influence catch rates of an invasive fish

When sampling wild animals, the collection methods used can impact the number, sex, size, and behaviour of individuals captured and it is important to understand these impacts when making inferences about populations. While biases between different types of sampling gear are well recognized, biases that arise from using different models of the same gear type are often not considered. To test if different models of the same gear type influence the number and type of individuals collected, we first used two different models of the same gear type (silver and black minnow traps) to collect an invasive fish species, the round goby (Neogobius melanostomus). Next, we tested if any observed differences in capture rates between the different trap models could be explained by differences in: (a) colour, by painting silver traps black and deploying them in the field; and/or (b) retention, by quantifying fish exit rates from the different trap models in the laboratory. We found that silver traps captured approximately twice as many fish as black traps, and that the fish captured in silver traps were smaller on average. Next, we found when silver traps were painted black these now black traps still had catch rates comparable to regular silver traps. Lastly, we found that fish were 13 times more likely to exit from black traps compared to silver traps. Our results suggest that different models of the same gear type can differ substantially in terms of the number and type of individuals they capture. Being aware of these slight dimensional differences between models within a gear type and understanding how even small shape differences can impact the samples collected is important when assessing populations of wild animals and when comparing results and data across studies. To explore this issue further, we conducted a literature survey and identified that of the 275 studies identified that employed minnow traps, 37% provided no description of the traps employed. We advocate that researchers provide detailed information related to the sampling gear used when collecting wild animals to inform appropriate inferences and improve comparative analyses across studies.

Characterization of Fish Assemblages and Standard Length Distributions among Different Sampling Gears Using an Artificial Neural Network

Several sampling gears are used to collect fish in the lentic ecosystem. The collected fish differ in their characteristics and community structure depending on the sampling gear. The objectives of this study were to 1) compare the community structure of fish assemblages sampled using four sampling gears (kick net, cast net, gill net, and fyke net) in the Singal (SG), Yedang (YD), and Juam (JA) reservoirs, and 2) to understand the characteristics of fishes collected by each sampling gear. A total of 1887 individuals of 14 species, 9113 individuals of 15 species, and 9294 individuals of 27 species were collected, respectively, from the SG, YD, and JA reservoirs. Among the four sampling gears tested, the fyke net collected the largest numbers of species and individuals, while the gill net collections had the highest diversity index. The results obtained with the self-organizing map (SOM) provided a more detailed characterization of the sampled fish than the metrics that are typically used to evaluate sampling gears. In particular, SOM analysis showed a similar pattern of the standard length of fish and sampling gear. Since each sampling gear has unique characteristics, the selection of an appropriate sampling gear should be based on the study objectives and features of the sampling sites.

Combining sampling gear to optimally inventory species highlights the efficiency of eDNA metabarcoding

AbstractBiodiversity surveys may require the use of multiple types of sampling gear to maximize the efficiency of species detections, yet few studies have investigated how to optimally distribute effort among gear. In this study, we conducted eDNA metabarcoding and capture‐based sampling surveys (electrofishing, fyke netting, gillnetting, and seining) to sample fish species richness in a large northern temperate lake. We evaluated the success of the sampling methods individually and in combination to determine the allocation of effort and cost across sampling gear that provides the optimal approach for lake‐wide species inventories. We found that eDNA metabarcoding detected more species than any other sampling method, including 11 species that were not detected with any capture‐based approach. Optimal gear combination analyses revealed that detected species richness is maximized when most of the effort or budget is allocated to eDNA metabarcoding, with smaller allocations to seining and fyke netting. eDNA metabarcoding and capture sampling gear showed similar patterns of spatial heterogeneity in the fish community across habitat types, with pelagic samples forming a group that was distinct from nearshore samples. Our results indicate that eDNA metabarcoding is a rapid and cost‐efficient tool for biodiversity monitoring and that assessing the complementarity of multiple sampling types can inform the development of optimal approaches for measuring fish species richness.

Variability in micronekton active carbon transport estimates on the Southwest Coast of Oahu using three different sampling gears

Micronekton were sampled off the southwest coast of Oahu in October of 2004 as part of the North Pacific Marine Sciences Organization (PICES) Micronekton Sampling Gear Intercalibration experiment (MIE-1). The initiative employed three different micronekton sampling gears: the Cobb Trawl, Isaacs–Kidd midwater trawl (IKMT), and Hokkaido University frame trawl (HUFT). Micronekton catches from the three sampling gears were used to quantify the rates of active carbon transport. We evaluate the differences in total active carbon transport assessed using the data collected by the three gears, with a particular emphasis on taxa- and length-specific differences across gears. The estimates of total active carbon transport derived from the HUFT were significantly lower than those derived from the IKMT. We detected no significant difference between estimates derived using the IKMT and Cobb and the Cobb and HUFT. Dominant groups contributing to active carbon transport varied across sampling gears (Cobb: myctophids and cephalopods; HUFT: decapods and stomatopods; IKMT: cephalopods, myctophids, and decapods); however, no significant differences in the contribution of individual taxa to active carbon transport across sampling gears were detected (e.g., the myctophid active carbon transport contribution for the Cobb was not significantly different from that of the IKMT). Pairwise ratios revealed size-specific differences in the contribution to total flux across gears. As expected, the HUFT and IKMT estimates of active carbon transport were higher for smaller size classes, while estimates from the Cobb were more evenly distributed across size classes. These findings suggest that the differences in total active carbon transport from the three sampling gears are driven primarily by size-based sampling biases, which produce significant differences in biomass estimates. This study provides a foundation upon which future active carbon transport studies may be adjusted to address sampling gear biases.

Kemer Koyu Mollusca faunası (Marmara Denizi)

In the present study, mollusc species distributed in the Kemer Bay (Sea of Marmara) were determined seasonally (November, February, May, and August) in the years 2010 and 2011. The benthic samples were collected from different biotopes in 16 stations at depth ranging between 0.2 and 5 m by using a quadrate sampling gear. As a result of the evaluation of collected benthic materials, a total of 66 mollusc species belonging to 3 classes (Polyplacophora, Gastropoda, and Bivalvia) were identified. When the number of species and individuals are compared based on the seasons, maximum number of species (46 species) and individuals (168825 ind. m-2) were counted in autumn and spring, respectively. Mytilus galloprovincialis (Lamarck, 1819) and Mytilaster lineatus (Gmelin, 1791) were the most abundant species in all the seasons. Whereas, Bittium reticulatum (da Costa, 1778), Rissoa membranacea (Montagu, 1803), and Tricolia pullus pullus (Linnaeus, 1758) were characterized as species having the highest frequency index values in all seasons.

Development of Survey Methods to Effectively Sample Juvenile Red Snapper along the Atlantic Coast of Florida

AbstractUnderstanding of the early life history of Red Snapper Lutjanus campechanus throughout the U.S. Atlantic Ocean (hereafter, “Atlantic”) is limited, in part, due to the paucity of juveniles (&lt;150 mm TL) collected in long‐term fishery‐independent surveys in the region. This is in sharp contrast to the Gulf of Mexico (hereafter, “Gulf”), where juvenile Red Snapper have been consistently collected in surveys. This apparent regional disparity is poorly understood. Red Snapper nursery habitats may differ between the Atlantic and the Gulf, previous Atlantic surveys may not have included important nursery habitat, or sampling gear used in the Atlantic may not be effective at collecting juveniles. A 2‐year fishery‐independent study was conducted along the Atlantic coast of Florida to test whether juvenile Red Snapper could be effectively sampled with two types of gear not commonly used there: a semi‐balloon trawl like those used in long‐term groundfish surveys in the Gulf and small‐mesh Z‐traps. In total, 194 Red Snapper were collected in the trawl samples and 202 Red Snapper were collected in the Z‐trap samples—mostly juveniles (age 0 and age 1) captured in nearshore waters (&lt;30 m deep). Like the Gulf, shallow coastal waters in the Atlantic likely function as nursery habitat for Red Snapper. Primarily small age‐0 Red Snapper were collected during trawl sampling, which targeted unconsolidated nonreef habitats, whereas larger age‐0 and age‐1 and older Red Snapper were collected during trap sampling, which targeted hard‐bottom reef habitats. Although this study represents the most successful sampling of juvenile Red Snapper in the Atlantic to date, further research is required to delineate the northern and southern extents of Red Snapper nursery grounds. Nevertheless, our results provide a framework for developing a fishery‐independent survey that targets juvenile Red Snapper in the Atlantic to provide valuable data for quantifying recruitment and monitoring the status and recovery of this economically important species.

A Transdisciplinary Approach Towards Studying Direct Mortality Among Demersal Fish and Benthic Invertebrates in the Wake of Pulse Trawling

Between 2009 and 2020, beam trawlers in the North Sea switched to electrical stimulation to target sole (Solea solea). The transition to pulse trawling raised widespread concern about possible adverse effects of electrical stimulation on marine organisms. Environmental NGO’s and small scale fishers claimed that it would electrocute marine life and create a ‘graveyard’ in the wake of pulse trawlers. This paper uses realistic field experiments to investigate the ‘graveyard’ hypothesis. In cooperation with fishers, a field experiment was designed where we simultaneously sampled marine organisms in the wake of pulse trawlers and in untrawled control areas. The impact was quantified by estimating the direct mortality among three dominant fish species and four dominant invertebrate species. In total, nine experimental tows were conducted in two years. Direct mortality among fish and invertebrates was low (0-10%) and did not differ between the pulse trawl track and the untrawled controls. Equally, no impact of the pulse trawl was found on external damages and vitality scores. The limited effects observed are likely due to the mechanical impact of the pulse and the sampling gear. The results of experiment do not support the claim that pulse trawling results in mass mortality among marine organisms in the trawl track. Throughout the research period, the concerns of small-scale fishers on pulse fishing shifted from being focused on biological effects to political and managerial issues. This can partly be attributed to the engagement in and the results of our research and has increased its credibility and salience. By integrating fishers’ knowledge and examining their perceptions through transdisciplinary research, we were able to show the importance of untangling the intricate relation between perceived knowledge gaps and political or management related concerns.

A Deep Convolutional Generative Adversarial Networks-Based Method for Defect Detection in Small Sample Industrial Parts Images

Online defect detection in small industrial parts is of paramount importance for building closed loop intelligent manufacturing systems. However, high-efficiency and high-precision detection of surface defects in these manufacturing systems is a difficult task and poses a major research challenge. The small sample size of industrial parts available for training machine learning algorithms and the low accuracy of computer vision-based inspection algorithms are the bottlenecks that restrict the development of efficient online defect detection technology. To address these issues, we propose a small sample gear face defect detection method based on a Deep Convolutional Generative Adversarial Network (DCGAN) and a lightweight Convolutional Neural Network (CNN) in this paper. Initially, we perform data augmentation by using DCGAN and traditional data enhancement methods which effectively increase the size of the training data. In the next stage, we perform defect classification by using a lightweight CNN model which is based on the state-of-the-art Vgg11 network. We introduce the Leaky ReLU activation function and a dropout layer in the proposed CNN. In the experimental evaluation, the proposed framework achieves a high score of 98.40%, which is better than that of the classic Vgg11 network model. The method proposed in this paper is helpful for the detection of defects in industrial parts when the available sample size for training is small.

Sentinels of Seabed (SoS) indicator: Assessing benthic habitats condition using typical and sensitive species

Indicators are key tools used to assess the ecological status of the environment for ecosystem based management. Anthropogenic disturbances produce changes to habitat condition, which include modifications in species composition and their functions. Monitoring a group of sentinel species (from a taxonomic and functional point of view) provides useful insights into benthic habitat condition. Here, a new indicator, Sentinels of the Seabed (SoS) is proposed to assess state of benthic habitats using “sentinel” species (species which are characteristic of a habitat and sensitive to a given pressure). The selection of these sentinel species has two stages. First, a ‘typical species set’ is computed using intra-habitat similarity and frequency under reference conditions. Second, the ‘sentinel species set’ is generated by selecting the most sensitive species from the typical species set. This selection is made using specific indexes able to assess species sensitivity to a particular pressure. The SoS indicator method was tested on six case studies and two different pressure types (trawling disturbance and pollution), using data from otter trawl, box-corer and Remote Operate Vehicle images. In each scenario, the SoS indicator was compared to the Shannon-Wiener diversity index, Margalef index and total biomass, being the only metric, which showed the expected significant negative response to pressure in all cases. Our results shows that SoS was highly effective in assessing benthic habitats status under both physical and chemical pressures, regardless of the sampling gear, the habitat, or the case study, showing a great potential to be a useful tool in the management of marine ecosystems.