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FIGURE 6 in Knowledge connections for conservation of the Atlantic Goliath Grouper, Epinephelus itajara: records of tropical Brazilian coast
FIGURE 6 | A. Percentage of Atlantic Goliath Grouper (AGG) Epinephelus itajara registered along the coast of Alagoas, arranged in length category and; B. Percentage of individuals registered alive and dead (fished and stranded), along the division of the coast of Alagoas. C. Percentage of AGG recorded along the coast of Alagoas, distributed by habitats. Stranded animals also make up the graph. D. Distribution of AGG with estimated TL, arranged along the coast of Alagoas. Boxplots present the maximum and minimum limits (upper and lower rush). In the interquartile range are the third and first quartiles at the ends, median and mean (x) of the total length of the subjects. Colordifferentiated total length (TL) categories and separated by habitats.
FIGURE 5 in Knowledge connections for conservation of the Atlantic Goliath Grouper, Epinephelus itajara: records of tropical Brazilian coast
FIGURE 5 | A. Sightings of individuals of Atlantic Goliath Grouper (AGG) Epinephelus itajara in Alagoas, between 1960 and 2021, arranged by habitat and; B. Distribution of AGG with estimated Total Length arranged by habitat. Stranded animals also make up the boxplot. Habitats are separated by color, boxplots presents the maximum and minimum limits (upper and lower rush). In the interquartile range are the third and first quartiles at the ends, median and mean (x) of the AGG total length.
FIGURE 4 in Knowledge connections for conservation of the Atlantic Goliath Grouper, Epinephelus itajara: records of tropical Brazilian coast
FIGURE 4 | Photographic records from the pre-moratorium period, from the 1970s, 1980s, and 1990s. All images are of captures made through spearfishing in shallow coastal reefs off the Alagoas coast.
FIGURE 1 in Knowledge connections for conservation of the Atlantic Goliath Grouper, Epinephelus itajara: records of tropical Brazilian coast
FIGURE 1 | Distribution of Atlantic Goliath Grouper (AGG) Epinephelus itajara from the coast of Alagoas. Sites with the occurrence of the AGG validated through photographic or scientific records (Image records); places cited by informants for the occurrence of the species (Reports). Alagoas State, Brazil. The Alagoas coast was separated into north, central and south sectors. The figure highlighted Federal Marine Protected Areas (MPA), on the north coast is the MPA "Costa dos Corais". To the south is located the Marine Extractive Reserve of Lagoa do Jequiá and Environmental Protection Area of Piaçabuçu in the extreme south of the coast.
Fig. 3 in New distribution records and conservation status of Atelopus seminiferus Cope, 1874: A Critically Endangered harlequin frog from northern Peru
Fig. 3. Updated distribution map of Atelopus seminiferus. Black dots indicate new localities reported in this study. Light green area corresponds the estimated Extent of Occurrence (ca. 2,520 km2) based on the new records presented here and the previously known localities. Numbers correspond to labels in Table 1. Map by Juan C. Cusi.
Fig. 3 in New distribution records and conservation status of Atelopus seminiferus Cope, 1874: A Critically Endangered harlequin frog from northern Peru
Fig. 3. Dorsal and ventral views of the holotype of Atelopus seminifeus (ANSP 11383), deposited in the herpetological collection at the Academy of Natural Sciences of Drexel University, Philadelphia. Photos courtesy of Ned Gilmore.
Fig. 2 in New distribution records and conservation status of Atelopus seminiferus Cope, 1874: A Critically Endangered harlequin frog from northern Peru
Fig. 2. (A) A pair of Atelopus seminiferus in amplexus, found between El Carmen and La Esperanza [not collected]. Photo by Fredi Sangama and Florencio León. (B) Dorsal coloration pattern of a female MUSM 33328. (C) Ventral coloration pattern in a male MUSM 33327. (D) Ventral coloration pattern in a female MUSM JCM H-24. (D) El Carmen village in Alto Mayo Protected Forest, Rioja province, San Martin (E). Photos B‒E by Juan C. Cusi.
Fig. 1 in New distribution records and conservation status of Atelopus seminiferus Cope, 1874: A Critically Endangered harlequin frog from northern Peru
Fig. 1. Distribution of Atelopus seminiferus in the Mayo River basin, San Martin, Peru. Yellow polygon corresponds to geographic range estimated by IUCN. Compare with Fig. 3, which shows proposed new polygon based on results from this study. Map by Juan C. Cusi.
The CWR richness dataset of manuscript "ENHANCING IN SITU CONSERVATION OF CROP WILD RELATIVES FOR FOOD AND AGRICULTURE IN LITHUANIA"
<p>The CWR National Inventory database has been created by combining data from the Database of EU Habitat Mapping in Lithuania (BIGIS), the Herbarium Database of the Nature Research Centre (BILAS), the Lithuanian Vegetation Database (EU-LT-001), and the Global Biodiversity Information Facility (GBIF). It was used to calculate CWR richness in 4 by 4 kilometres grid cell. The dataset contains three GIS files (.shp) - Boundaries of Lithuania, CWR richness in grid cells and 45 potential genetic reserve sites.</p>
Replication Data and Analyses for: J. Monsimet, S. Sjögersten, N.J. Sanders, M. Jonsson, J. Olofsson & M. Siewert, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, Remote Sensing in Ecology and Conservation.
<p>This dataset corresponds to the article: <strong>"Jérémy Monsimet*¹, Sofie Sjögersten², Nathan J. Sanders³, Micael Jonsson¹, Johan Olofsson¹, Matthias Siewert¹, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, <em>Remote Sensing in Ecology and Conservation</em>"</strong></p> <p>DOI: <a href="https://doi.org/10.1002/rse2.400" target="_blank" rel="nofollow noreferrer noopener">10.1002/rse2.400</a></p> <p>1 Department of Ecology and Environmental Science, Umeå University, Sweden<br>2 School of Biosciences, University of Nottingham, Loughborough, UK<br>3 Department of Ecology and Evolutionary Biology, University of Michigan, US</p> <p>The gitlab repository of this dataset is available at: <a href="https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/">https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/</a></p> <p>In this repository, you will find the analyses and results presented in the paper. In each folder, there is a html file that can be read after downloading locally the whole folder. You can either run the .qmd file used to produce the html file or walk through the html files (see the readme.md for more information).</p> <p>Paper abstract:</p> <p>High‐resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the<em> Betula nana</em> vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient‐limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi‐sensor data and deep learning for efficient ecological tracking and monitoring of mound‐building ants and their spatial impact.</p>
Data from: Growth and longevity of the endangered freshwater pearl mussel (Margaritifera margaritifera): Implications for conservation and management
<p>Key life-history data, such as growth and age, are necessary to effectively manage and conserve threatened freshwater mussel species. Traditionally growth and age studies require large yet destructive sample sizes covering all age classes. Such methods pose a risk to populations of conservation concern, and therefore alternative methods that need only limited sample sizes are necessitated to prevent further threats to such populations. We applied retrospective shell growth at age reconstructions to 98 critically endangered freshwater pearl mussel (FPM) individuals from 34 populations across Finland and Sweden, enabling the use of extremely small sample sizes (n = 1–6 per population). We compared the performance of six different growth models with the reconstructed size-at-age data across FPM juvenile (<20 years old) and adult life stages. The growth reconstruction model showed reasonable skill in reconstructing FPM growth patterns. The von Bertalanffy model was shown to be a good general descriptor of growth for FPM, but it systematically underestimated the asymptotic size. The power law model was the most accurate in estimating juvenile growth (lowest deviances from the size-at-age data). FPM showed great variability in longevity (A<sub>max</sub> = 54–254 years) and growth constant k (0.018– 0.057 year<sup>-1</sup>). Our results show that reasonable estimates of growth can be attained even when sample sizes are extremely limited. The results can be further applied to gain knowledge on the population's age structure, size at maturation, and recovery potential. The methodology is applicable to other freshwater mussel species of conservation concern.</p>
Fig. 2 in Climatic niche, natural history, and conservation status of the Porthole Treefrog, Charadrahyla taeniopus (Günther, 1901) (Anura: Hylidae) in Mexico
Fig. 2. Current (A), and future 2050 (B) and 2070 (C) scenarios of the climatic niche of Charadrahyla taeniopus in Sierra Madre Oriental province (degraded area). The color scale indicates the probability of occupancy for the species.
Fig. 1 in Climatic niche, natural history, and conservation status of the Porthole Treefrog, Charadrahyla taeniopus (Günther, 1901) (Anura: Hylidae) in Mexico
Fig. 1. Female individual of Charadrahyla taeniopus (A) in a cloud forest and (B) at Tenango de Doria, Hidalgo, Mexico. Photos by Uriel Hernández-Salinas (A) and Raciel Cruz-Elizalde (B).
Fig. 3C in The distribution and conservation status of the Dwarf Marsupial Frog (Flectonotus fitzgeraldi, Anura, Hemiphractidae) in Trinidad, Tobago, and Venezuela
Fig. 3C. Map showing both presence and absence of Flectonotus fitzgeraldi as reported in this study in Venezuela. Location numbers correspond to those in Table 2.
Fig. 2 in The distribution and conservation status of the Dwarf Marsupial Frog (Flectonotus fitzgeraldi, Anura, Hemiphractidae) in Trinidad, Tobago, and Venezuela
Fig. 2. Maximum numbers of frogs calling per 10 minutes during eight transects along the Morne Bleu ridge in 2006 (filled circles: Flectonotus fitzgeraldi; empty circles: Pristimantis urichi).
Fig. 1 in The distribution and conservation status of the Dwarf Marsupial Frog (Flectonotus fitzgeraldi, Anura, Hemiphractidae) in Trinidad, Tobago, and Venezuela
Fig. 1. Best Maximum Likelihood (ML) tree of Flectonotus fitzgeraldi populations from Trinidad, Tobago, and Venezuela. Values at the nodes are comprised of ML posterior probabilities (> 75%) and Bayesian Inference probabilities (> 95%). In the Medium-Joining network, numbers in circles represent the number of sequences with the same haplotype and dashes are number of substitutions.
Fig. 3B in The distribution and conservation status of the Dwarf Marsupial Frog (Flectonotus fitzgeraldi, Anura, Hemiphractidae) in Trinidad, Tobago, and Venezuela
Fig. 3B. Map showing both presence and absence of Flectonotus fitzgeraldi as reported in this study in Tobago. Location numbers correspond to those in Table 2.
Fig. 3A in The distribution and conservation status of the Dwarf Marsupial Frog (Flectonotus fitzgeraldi, Anura, Hemiphractidae) in Trinidad, Tobago, and Venezuela
Fig. 3A. Map showing both presence and absence of Flectonotus fitzgeraldi as reported in this study in Trinidad. Location numbers correspond to those in Table 2.
Supporting data for: Angiosperms in a conservation area in the semiarid region of Paraíba State, Northeastern Brazil
<h3>Title: Angiosperms in a conservation area in the semiarid region of Paraíba State, Northeastern Brazil.</h3> <p>Contact information: Bernardo de Farias Rocha, bernardorocha284@gmail.com, and José Iranildo Miranda de Melo, tournefort@gmail.com, Universidade Estadual da Paraíba.</p> <p>Figures and table produced to illustrate an article, disponibilized as supporting data.</p> <p>Geographical Coordinates of the Study Area: 6º27'S, 36º18'W</p>
Fig. 2. A in First population assessment of two cryptic Tiger Geckos (Goniurosaurus) from northern Vietnam: Implications for conservation
Fig. 2. A: Macrohabitat and B: Microhabit of Goniurosaurus luii in Ha Lang District, Cao Bang Province, North Vietnam; specimens of G. luii from C: Cao Bang Province and D: Lang Son Province (new record). Photos Mona van Schingen, Marta Bernardes, and Tao Thien Nguyen.
ScienceDex guides
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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.