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6,157 results for “knowledge”
Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions
<p><span>Reliable maps of species distributions are fundamental for biodiversity research and conservation. Range maps created by the International Union for Conservation of Nature (IUCN) Red List are often considered authoritative but may not match species occurrence data. We tested concordance between occurrences from camera trap surveys and predicted occurrence from IUCN maps for 510 medium- to large-bodied mammalian species in 80 camera-trap sampling areas. Across all areas, cameras detected 39% of the species that were expected to occur based on IUCN ranges. The probability of mismatches between camera traps and IUCN range maps was significantly higher for smaller-bodied mammals and habitat specialists in the Neotropics and Indomalaya, and in areas with shorter canopy forests. Our results indicate that in many areas within their range map distributions species may be rare or absent. We suggest that combining range map data with accumulating data from ground-based biodiversity sensors, such as camera traps, acoustic recorders, and eDNA surveys, provides a richer knowledge base for conservation mapping and planning.</span></p>
Figure 3 in Biology of jungle perch, Kuhlia rupestris, identification of threats and knowledge gaps to improve local and global management
Figure 3. – Life cycle of K. rupestris and conservation issues. Within the cycle (blue area), the solid black line corresponds to the freshwater life phase. The lower and upper grey discontinuous lines correspond to the larval marine and estuarine phases of the species respectively. Outside the cycle, in orange, the known and suspected threats on the different life phases of the species (Gelineau et al., modified, 2015).
Figure 1 in Biology of jungle perch, Kuhlia rupestris, identification of threats and knowledge gaps to improve local and global management
Figure 1. – Distribution of K. rupestris in the Indo-Pacific zone (modified from Feutry, 2012a). In dotted line, the presumed natural range of the species and in solid lines, localities with a high likelihood of occurrence or known occurrence.
Figs 11–18 in Addenda To The Knowledge Of Beetles (Insecta: Сoleoptera) Of Kaliningrad Region (Western Russia): New Faunistic Records
Figs 11–18. The photographs of the living specimens in nature (Kaliningrad Region): 11 – Pilemostoma fastuosum; 12 – Palaeocallidium coriaceum; 13 – Rhagium sycophanta; 14 – Ropalopus macropus; 15 – Cleopus pulchellus; 16 – Lixus filiformis; 17 – Otiorrhynchus armadillo, female (left box – aedeagus from collected specimen of 2020 year); 18 – Rhopalapion longirostre.
Fig. 22. Callimetopus Fig. 23. Callimetopus Fig. 24 in To The Knowledge Of The Genus Callimetopus Blanchard, 1853 (Coleoptera: Cerambycidae)
Fig. 22. Callimetopus Fig. 23. Callimetopus Fig. 24. Callimetopus zhantievi tsinkevichi Barševskis, 2018. variolosus (Schultze, 1920). Barševskis, 2015
Figs 3–10 in Addenda To The Knowledge Of Beetles (Insecta: Сoleoptera) Of Kaliningrad Region (Western Russia): New Faunistic Records
Figs 3–10. The photographs of the living specimens in nature (Kaliningrad Region): 3 – Stenolophus teutonus; 4 – Potamophilus acuminatus; 5 – Hylis olexai; 6 – Denticollis rubens; 7 – Melandrya barbata; 8 – Mycetophagus ater; 9 – Mordellaria aurofasciata; 10 – Boros schneideri.
Fig. 19. Callimetopus shavrini Fig. 20. Callimetopus stanleyi Fig. 21 in To The Knowledge Of The Genus Callimetopus Blanchard, 1853 (Coleoptera: Cerambycidae)
Fig. 19. Callimetopus shavrini Fig. 20. Callimetopus stanleyi Fig. 21. Callimetopus tagalus Barševskis, 2015 Dela Cruz, Adorada, 2012. (Heller, 1899)
Fig. 13. Callimetopus nigritarsis Fig. 14. Callimetopus Fig. 15 in To The Knowledge Of The Genus Callimetopus Blanchard, 1853 (Coleoptera: Cerambycidae)
Fig. 13. Callimetopus nigritarsis Fig. 14. Callimetopus Fig. 15. Callimetopus ornatus (Pascoe, 1865) ochreosignatus Breuning, 1958 (Schultze, 1934).
Fig. 10. Callimetopus longicollis Fig. 11. Callimetopus Fig. 12 in To The Knowledge Of The Genus Callimetopus Blanchard, 1853 (Coleoptera: Cerambycidae)
Fig. 10. Callimetopus longicollis Fig. 11. Callimetopus Fig. 12. Callimetopus mindorensis (Schwarzer, 1931). lumawigi Breuning, 1980. Dela Cruz, Adorada, 2012
Fig. 4. Callimetopus cynthia Fig. 5. Callimetopus Fig. 6 in To The Knowledge Of The Genus Callimetopus Blanchard, 1853 (Coleoptera: Cerambycidae)
Fig. 4. Callimetopus cynthia Fig. 5. Callimetopus Fig. 6. Callimetopus illecebrosus (Thomson, 1865). danilevskyi Barševskis, 2015. (Pascue, 1865).
Fig. 7. Callimetopus juliae Fig. 8. Callimetopus kalninsi Fig. 9 in To The Knowledge Of The Genus Callimetopus Blanchard, 1853 (Coleoptera: Cerambycidae)
Fig. 7. Callimetopus juliae Fig. 8. Callimetopus kalninsi Fig. 9. Callimetopus Barševskis, 2016. Barševskis, 2018 laterivitta (Heller, 1915).
Fig. 1. Callimetopus albatus Fig. 2. Callimetopus Fig. 3 in To The Knowledge Of The Genus Callimetopus Blanchard, 1853 (Coleoptera: Cerambycidae)
Fig. 1. Callimetopus albatus Fig. 2. Callimetopus Fig. 3. Callimetopus (Newman, 1841). antonkozlovi Barševskis, 2016. bilineatus Vives, 2015.
Fig. 16. Callimetopus Fig. 17. Callimetopus Fig. 18 in To The Knowledge Of The Genus Callimetopus Blanchard, 1853 (Coleoptera: Cerambycidae)
Fig. 16. Callimetopus Fig. 17. Callimetopus Fig. 18. Callimetopus panayanus (Schultze, 1920). rhombiferus (Heller, 1913). samarensis Vives, 2012.
Fishers' ecological knowledge of the Lower Ucayali
<p>Data gathered for: <strong>Fishers' ecological knowledge points to fishing-induced changes in the Peruvian Amazon (2024)</strong><span><span><span><span><strong>. <em>Ecological Applications</em></strong></span></span></span></span></p> <div> <p>Scientists increasingly draw on fishers' ecological knowledge (FEK) to gain a better understanding of fish biology and ecology and inform options for fisheries management. We report on a study of FEK among fishers along the Lower Ucayali River in Peru, a region of exceptional productivity and diversity, which is also a major supplier of fish to the largest city in the Peruvian Amazon. Given a lack of available scientific information on stocks status, we sought to identify temporal changes in the composition and size of exploited species by interviewing fishers from 18 communities who vary in years of fishing experience since the mid-1950s. We develop four FEK-based indicators to assess changes in the fish assemblage and compare findings with landings data. </p> <p>We find an intensification of fishing gear deployed over time, spatiotemporal shifts in the fish assemblage, and reported declines in species weight, which point to a fishing-down process with declines across multiple species. This finding is reflected in a shifting baseline among our participants, whereby younger generation of fishers have different expectations regarding the distribution and size of species. Our study points to the importance of spillover effects from the nearby Pacaya-Samira National Reserve and community initiatives to support the regional fishery and the supply of fish to city markets. Reference to fishers' knowledge also suggests that species decline is likely underreported in aggregated landings data. </p> <p>The dataset contains information derived from fishers' interviews as well as a subset of socioeconomic information gathered during follow-up household surveys. Additional information gathered during household surveys conducted by the Peruvian Amazon Rural Livelihoods and Poverty (PARLAP) project (<a href="http://www.parlap.geog.mcgill.ca/">https://parlap.geog.mcgill.ca</a>) between 2014 and 2016 is also included. Landings data included in this study are restricted and not available publicly. The aggregated dataset of landings in Loreto from 1984 to 2016 is the property of the Dirección Regional de la Producción Loreto. Data are available to qualified researchers from Dirección Regional de la Producción Loreto by contacting the Director, whose contact information is available at <a href="https://www.gob.pe/institucion/regionloreto/funcionarios">https://www.gob.pe/institucion/regionloreto/funcionarios</a>. </p> </div>
Figigure 1. Phyla nodiflora var. nodiflora. A - B in Increasing the knowledge on the distribution of Phyla (Verbenaceae): a new record for the state of Santa Catarina, Southern Brazil
Figigure 1. Phyla nodiflora var. nodiflora. A - B. Habitat and habit; C. Leaves; D - F. Florescences; G. Map showing the new record (red circle) for the state of Santa Catarina, Brazil (Wegener & Garcia 138 - LAG). Acronyms (map): PR = Paraná; SC = Santa Catarina; RS = Rio Grande do Sul.
Fig. 7 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 7. Ordination plot of the correspondence analysis (first two axes) based on fishermen's answers about fishing season of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 6 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 6. Ordination plot of the correspondence analysis (first two axes) based on fishermen's answers about migratory routes of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 4 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 4. Trophic chain based on those food items and predators most cited by fishermen in the southeastern Brazilian coast for a) reef fishes and b) pelagic fishes. Numbers are percent of interviewed fishermen who mentioned each feeding interaction. Fish sizes are not in scale. Those feeding interactions that agree with reported feeding habits of these fishes in the biological literature are marked *(Randall, 1967; Berkeley & Houde, 1978; Menezes & Figueiredo, 1980; Sazima, 1986; Pipitone & Andaloro, 1995; Barreiros & Santos, 1998; Vasconcellos & Gasalla, 2001; Silvano, 2001; Silvano & Güth, 2006; Figueiredo & Vieira, 2005; Gibran, 2007).
Fig. 3 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 3. Main habitats of fishes according to fishermen in the southeastern Brazilian coast: percentages of fishermen who mentioned each habitat category are in Appendix 1. Double-headed arrows indicate that fishes occur in both habitats in horizontal space (e.g. open ocean and reefs), up and down arrows indicate that fishes occur in both habitats in vertical space (e.g., near the bottom and at the surface). Fish sizes are not in scale.
Fig. 1 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 1. Ordination plot of the correspondence analysis (first two axes) based on fishermen answers about uses of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.