An official website of the United States government
Uncertainties about the magnitude of bycatch in poorly assessed fisheries impede effective conservation management. In northern Peru, small-scale fisheries (SSF) bycatch negatively impacts marine megafauna populations and the livelihoods of fishers which is further elevated by the under-reporting of incidents. Within the last decade, accounts of entangled humpback whales (HBW) ( Megaptera novaeangliae ) off the northern coast of Peru have increased, while Eastern Pacific leatherback turtles (LBT) ( Dermochelys coriacea ) have seen over a 90% decline in nesting populations related in large part to bycatch mortality. By leveraging the experience and knowledge of local fishers, our research objectives were to use a low-cost public participation mapping approach to provide a spatio-temporal assessment of bycatch risk for HBW and LBT off two Peruvian fishing ports. We used an open-source, geographic information systems (GIS) model, the Bycatch Risk Assessment (ByRA), as our platform. Broadly, ByRA identifies high bycatch risk areas by estimating the intersection of fishing areas (i.e., stressors) with species habitat and evaluating the exposure and consequence of possible interaction between the two. ByRA outputs provided risk maps and gear risk percentages categorized as high, medium, and low for the study area and seven subzones for HBW in the austral winter and LBT in the austral summer. Overall, the highest bycatch risk for both species was identified within gillnet fisheries near the coast. Bycatch risk for most gear types decreased with distance from the coast. When we separated the ByRA model by port, our map outputs indicate that bycatch management should be port specific, following seasonal and spatial variations for HBW, and specific fishing gear impacts for HBW and LBT. Combined with direct bycatch mitigation techniques, ByRA can be a supportive and informative tool for addressing specific bycatch threats and marine megafauna conservation goals. ByRA supports a participatory framework offering rapid visual information via risk maps and replicable methods for areas with limited resources and data on fisheries and species habitat.
more »
« less
Towards a scientific community consensus on designating Vulnerable Marine Ecosystems from imagery
Management of deep-sea fisheries in areas beyond national jurisdiction by Regional Fisheries Management Organizations/Arrangements (RFMO/As) requires identification of areas with Vulnerable Marine Ecosystems (VMEs). Currently, fisheries data, including trawl and longline bycatch data, are used by many RFMO/As to inform the identification of VMEs. However, the collection of such data creates impacts and there is a need to collect non-invasive data for VME identification and monitoring purposes. Imagery data from scientific surveys satisfies this requirement, but there currently is no established framework for identifying VMEs from images. Thus, the goal of this study was to bring together a large international team to determine current VME assessment protocols and establish preliminary global consensus guidelines for identifying VMEs from images. An initial assessment showed a lack of consistency among RFMO/A regions regarding what is considered a VME indicator taxon, and hence variability in how VMEs might be defined. In certain cases, experts agreed that a VME could be identified from a single image, most often in areas of scleractinian reefs, dense octocoral gardens, multiple VME species’ co-occurrence, and chemosynthetic ecosystems. A decision flow chart is presented that gives practical interpretation of the FAO criteria for single images. To further evaluate steps of the flow chart related to density, data were compiled to assess whether scientists perceived similar density thresholds across regions. The range of observed densities and the density values considered to be VMEs varied considerably by taxon, but in many cases, there was a statistical difference in what experts considered to be a VME compared to images not considered a VME. Further work is required to develop an areal extent index, to include a measure of confidence, and to increase our understanding of what levels of density and diversity correspond to key ecosystem functions for VME indicator taxa. Based on our results, the following recommendations are made: 1. There is a need to establish a global consensus on which taxa are VME indicators. 2. RFMO/As should consider adopting guidelines that use imagery surveys as an alternative (or complement) to using bycatch and trawl surveys for designating VMEs. 3. Imagery surveys should also be included in Impact Assessments. And 4. All industries that impact the seafloor, not just fisheries, should use imagery surveys to detect and identify VMEs.
more »
« less
- PAR ID:
- 10539927
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- PeerJ
- Date Published:
- Journal Name:
- PeerJ
- Volume:
- 11
- ISSN:
- 2167-8359
- Page Range / eLocation ID:
- e16024
- Subject(s) / Keyword(s):
- Vulnerable marine ecosystems, Significant adverse impacts, Areas beyond national jurisdiction, Deep-Sea imagery, VME indicator taxa
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Wild orangutan populations are estimated to have declined by well over 50% in the last 60 years. Thus, a rapid, reliable survey method that can be used across orangutan habitat types is needed to track orangutan population density. Individual, adult orangutans build a new nest each night so orangutan populations have historically been evaluated through systematic, ground-based, nest surveys. However, orangutan nest surveys are costly, time-consuming, require some degradation of the forest, and are restricted to areas that are accessible by ground teams. In 2016-2017, we pilot-tested the use of drones to survey orangutan populations in Gunung Palung National Park, Borneo, Indonesia, determining that nests can be spotted and counted from the imagery collected. In 2018-2019, drone imagery for 50 transects was obtained and analyzed to calculate population density. Nests in the images were classified into nest degradation categories to match ground methods. On average, fewer nests were found using drone imagery analysis than ground surveys. We calculated habitat specific conversion factors for drone nest surveys, based on our ground-truthing, to estimate orangutan population densities. We compare the density values using these two methods and demonstrate the validity of using drones as an effective technique for estimating orangutan population size. Data were compared to completed phenological surveys and showed close correspondence between nest density and fruit availability. The drone imagery will allow for more rapid assessment of new or more intensive human impacts on the land, providing further insight into what conservation efforts are needed to protect orangutan populations.more » « less
-
These data represent the diet composition of small pelagic fishes assessed by the Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) project. The six species of fish in this dataset represent a subset of the species collected in bottom trawls conducted by the NOAA Fisheries Northeast Ecosystems Surveys from Cape Hatteras to the Gulf of Maine. Sampling occurred in the Spring and Fall seasons. Fish were frozen and stomach content analyses were conducted by the Fisheries Oceanography and Larval Fish Ecology Lab at the Woods Hole Oceanographic Institution. Data are counts and length measurements for prey items examined under a dissecting microscope. Prey species were matched to the lowest taxonomic level in the Integrated Taxonomic Information System (ITIS) for scientific name and taxonomic serial number. The dataset was supplemented with geospatial and temporal information from NOAA Fisheries trawl databases.more » « less
-
Discards from commercial fisheries have been linked to detrimental effects on ecosystems and stocks of living marine resources. Understanding spatial and temporal patterns of discards may assist in devising regulatory practices and mitigation strategies and promote sustainable management policies. This study investigates data from bycatch monitoring programs using a machine learning approach. We used a gradient boosting classifier for describing catch and bycatch patterns in the U.S. Mid-Atlantic Black Seabass (Centropristis striata), Summer Flounder (Paralichthys dentatus), Scup (Stenotomus chrysops), and Longfin Squid (Doryteuthis pealeii) fisheries. We used oceanographic, biological, spatial, and fisheries data as explanatory model features. We found positive associations between target species volume and bycatch. Although we found that sea surface temperature and year were important model features, the direction of impact of those predictors was variable. From our findings, we conclude that machine learning approaches are promising in supplementing traditional methodologies, especially with the increase in data availability trends.more » « less
-
Herbarium sheets present a unique view of the world's botanical history, evolution, and biodiversity. This makes them an all–important data source for botanical research. With the increased digitization of herbaria worldwide and advances in the domain of fine–grained visual classification which can facilitate automatic identification of herbarium specimen images, there are many opportunities for supporting and expanding research in this field. However, existing datasets are either too small, or not diverse enough, in terms of represented taxa, geographic distribution, and imaging protocols. Furthermore, aggregating datasets is difficult as taxa are recognized under a multitude of names and must be aligned to a common reference. We introduce the Herbarium 2021 Half–Earth dataset: the largest and most diverse dataset of herbarium specimen images, to date, for automatic taxon recognition. We also present the results of the Herbarium 2021 Half–Earth challenge, a competition that was part of the Eighth Workshop on Fine-Grained Visual Categorization (FGVC8) and hosted by Kaggle to encourage the development of models to automatically identify taxa from herbarium sheet images.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7717/peerj.16024
