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213 results for “species monitoring”
Figure 1 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 1. Locations of the monitoring sites. A: North of Orkney Mainland and northern isles, B: Scapa Flow and southern isles. For corresponding site names refer to Supplementary material Table S1.
Figure 3 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 3. The total number of sites for which each non-native or cryptogenic species has been recorded for each sampling method for 2016 and 2017. NB: Only species recorded in these two years are reported in this figure. Abbreviations: Cm: Caprella mutica, As: Ascidiella scabra, Bh: Bonnemaisonia hamifera, Sj: Schizoporella japonica, Ce: Corella eumyota, Aa: Ascidiella aspersa, Cp: Colpomenia peregrina, Cf: Codium fragile ssp. fragile, Ti: Tricellaria inopinata, Ah: Asterocarpa humilis, Bs: Botryllus schlosseri, Bl: Botrylloides leachii, Mh: Melanothamnus harveyi, Bf: Bugulina fulva, Dj: Dasysiphonia japonica, Jm: Jassa marmorata, Mi: Monocorophium insidiosum, Ma: Monocorophium acherusicum, Cs: Ctenodrilus serratus, Tj: Telmatogeton japonicus, Pa: Potamopyrgus antipodarum, Cb: Crassicorophium bonellii, Dl: Diplosoma listerianum.
Figure 2 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 2. The total number of non-native and cryptogenic species recorded at each monitoring location (2012–2017).
Рис. 1. Схема точек отбора проб на ООПТ в бассейне Амура Fig. 1. Map of sampling points at the protected areas in the Amur River Basin in Molting feathers and eggshells in monitoring rare bird species populations: Evidence from special protected natural territories
Рис. 1. Схема точек отбора проб на ООПТ в бассейне Амура Fig. 1. Map of sampling points at the protected areas in the Amur River Basin
Рис. 3. Соотношение концентраций биогенных микроэΛементов в очинах перьев (а) и скорΛупе яиц аистов (b), мг/кг Fig. 3. Concentratiom ratio of biogenic trace elements in the edges of feathers (a) and eggshells (b) of storks, mg/kg in Molting feathers and eggshells in monitoring rare bird species populations: Evidence from special protected natural territories
Рис. 3. Соотношение концентраций биогенных микроэΛементов в очинах перьев (а) и скорΛупе яиц аистов (b), мг/кг Fig. 3. Concentratiom ratio of biogenic trace elements in the edges of feathers (a) and eggshells (b) of storks, mg/kg
B in The monitoring of feather mites (Acari, Astigmata) of the Warbler (Aves: Sylviidae) species in the Kızılırmak delta, Samsun, Turkey
B rd spec es Figure 2. Number of the feather mite species identified on members of the family Sylviidae.
Data, code, and supplementary materials for Pearman P. B., Broennimann, O., et al. Monitoring species genetic diversity in Europe varies greatly and overlooks potential climate change impacts. Nature Ecology & Evolution
<p>The repository contains several archives of digital materials that were used and/or produced in the analyses presented in Pearman, P. B. and Broennimann et al. Monitoring species genetic diversity in Europe varies greatly and overlooks potential climate change impacts. <strong>Nature Ecology & Evolution</strong>, likely 2023. These archives include (1) Supplementary Materials files ; (2) Data and code to generate country-level maps and plots; and (3) data and code to generate all maps of species and joint climate niche marginality, all in G-zipped tar archives. Readme files are available in each archive to guide running of the scripts and identification of objects in the Supplementary Materials. Please see the paper for all co-authors names, and the methods, the results obtained, and discussion of their implications.</p> <p>This work is dedicated to the memory of our friend and colleague Michael Bruford (1963-2023).</p>
Data from: Optimizing passive acoustic monitoring (PAM) for Biodiversity Studies: using species-area relationship (SAR) to predict species richness
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Ndege Zetu: A dataset to compare bird species monitoring approaches in the Mt Kenya ecosystem
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Integrating presence-only and detection/non-detection data to estimate distributions and expected abundance of difficult-to-monitor species on a landscape-scale
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Population genomic and morphological datasets from: An evolutionary mosaic challenges traditional monitoring of a foundation species in a coastal environment - the Baltic Fucus vesiculosus
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Data from: DNA metabarcoding for biodiversity monitoring in a national park: screening for invasive and pest species
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Monitoring demography of resurrected populations of locally extinct and extant species to investigate drivers of species loss
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Fall 2013 grasshopper monitoring -- mid-marsh grasshopper abundance and species diversity at eight GCE LTER sampling sites
Grasshopper abundance and species diversity were investigated at eight sampling sites within the Georgia Coastal Ecosystems (GCE) LTER study area in August 2013. Visual surveys were conducted along 8 2m by 10m transects randomly allocated within the mid-marsh zone at each site. All grasshoppers observed within each transect were counted and identified to species, if possible. This survey was conducted as part of the GCE invertebrate monitoring program, and will be performed annually to assess long-term changes in relative species abundances across the GCE study area.
Figure 10 in Taxonomy of Micronesian monitors (Reptilia: Squamata: Varanus): endemic status of new species argues for caution in pursuing eradication plans
Figure 10. Varanus bennetti sp. nov., Ngarchelong, Palau (photo by Thibaud Aronson).
Improving citizen science data for long-term monitoring of plant species
<p>In 2012, a new volunteer-based recording scheme for vascular plants was launched in the Netherlands. Its purpose is to track the changes in the number of occupied 1-km grid cells for as many native plant species as possible between survey rounds of 8 years. We did not prescribe a strict field protocol to minimize variation in observer effort, but instead chose to statistically correct for this variation with occupancy models. These models require replicated visits to a grid cell per season, which was implemented by having two independent observers survey grid cells and record all plant species observed. Now that a first survey round has ended (2012–2019), we evaluate our approach, i.e. we tested whether the scheme has the potential to produce proper trend estimates. The number of occupied grid cells in the first round was estimated per species, using an occupancy model with day of year, visit duration and observer experience as covariates for detection. The detection probability, which was 0.43 on average, strongly depended on visit duration and day of year. It was possible to estimate the number of occupied grid cells quite precisely for several hundreds of species, such that the statistical power is expected to be high enough to detect changes of 10% between survey rounds. For rare species, however, the power to detect changes is expected to be quite low. We conclude that the approach works well, but further improvements are suggested.</p>
Fig. 2 in Data On Protected And Insufficiently Known Insect Species Obtained From The Invertebrate Monitoring In Latvia (2015 - 2016)
Fig. 2. The spatial arrangement scheme of the monitoring activities in one of the squares.
Fig. 1 in Data On Protected And Insufficiently Known Insect Species Obtained From The Invertebrate Monitoring In Latvia (2015 - 2016)
Fig. 1. The layout of invertebrate monitoring sites in Latvia.
Figure 4 in The monitoring of feather mites (Acari, Astigmata) of the Warbler (Aves: Sylviidae) species in the Kızılırmak delta, Samsun, Turkey
Figure 4. Monthly and annual comparison of the number of feather mite individuals.
Data from: Predicting bushmeat biomass from species composition captured by camera traps: implications for locally-based wildlife monitoring
<p>The 'StatAnalysis.zip' contains the data and model files. We used it for the four analyses below.</p> <p>First, we estimated population densities, the mean body mass and camera-trap capture rates of five main bushmeat targets in a rainforest of southeast Cameroon: Peters's duikers (<em>Cephalophus callipygus</em>), bay duikers (<em>C. dorsalis</em>), blue duikers (<em>Philantomba monticola</em>), brush-tailed porcupines (<em>Atherurus africanus</em>) and Emin's pouched rats (<em>Cricetomys emini</em>). Second, on the basis of the density and body mass estimates, we estimated bushmeat biomass—the total biomass of the five bushmeat species—and its spatial variation. Third, we calculated six bushmeat indicators based on the capture rate estimates. Lastly, we examined the correlation between bushmeat biomass and the indicators.</p> <p>The ZIP file consists of 16 R script files, three CSV files (in the 'data' subfolder) and 135 stan files (in the 'stan' subfolders). It also has two empty folders, 'figure' and 'res', where the figures and R objects of model results will be stored following the analyses. Please see the document 'README.txt' before performing the analysis. This text file gives the ZIP file structure and brief descriptions of the files.</p>
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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.