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Fig. 2 in Assessing the natural circulation of canine vector-borne pathogens in foxes, ticks and fleas in protected areas of Argentine Patagonia with negligible dog participation

Fig. 2. Abundance of Pulex irritans and Amblyomma tigrinum in grey foxes depending on the study area. (*) indicates significant differences.

opencc-by-4.0Apr 2019View details →
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Fig. 1 in Assessing the natural circulation of canine vector-borne pathogens in foxes, ticks and fleas in protected areas of Argentine Patagonia with negligible dog participation

Fig. 1. Map of Latin America, showing the study areas in the insert. Black circle: Bosques Petrificados National Park; grey circle: Monte León National Park.

opencc-by-4.0Apr 2019View details →
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High resolution Sea Surface Wind retrieval over coastal Protected Areas by means of Sentinel-1 data

<p><br> The algorithm used, i.e. SARWIND LG-Mod ver. v4.01 (see reference below), is aimed at producing the Sea Surface Wind (SSW), i.e. Speed and Direction, from a single co-polarized (VV or HH) SAR image. We used EW (Extended Wide) and IW (Interferometric Wide) Swath Mode GRD (Ground Range, Multi-Look, Detected) HR (High Resolution) Sentinel-1 images, with pixel spacings of 40m x 40m and 10m x 10m (azimuth x range) respectively. Associated auxiliary products were obtained from ESA SNAP 5.0 release. SSW fields were provided for the two coastal Protected Areas (PAs) named Camargue and Wadden Sea.</p> <p>Each output folder of the SARWIND LG-Mod results contains useful plots and the estimated SSW field, provided in the file &#39;SAR_Sigma0_pp_decimationL2P2Tn_gradientOptSobel_LGMod_Results.txt&#39; (pp = VV or HH; n = smoothing/decimation level), which is in the sub-folder &#39;LG-Mod_Theoretical_Results/Results_MEdegTHxx.xxx_Fisher (where xx.xxx is the final threshold applied). This txt file reports the following 19 columns:</p> <p><br> 1) LAT; 2) LON; 3) AZI; 4) RNG; [Location of the centre of the processed AOI]</p> <p>5) REF_U; 6) REF_V; 7) REF_W; 8) REF_D; [ECMWF reference wind, as U/V components and speed/direction]</p> <p>9) SAR_U; 10) SAR_V; 11) SAR_W; 12) SAR_D; [SARWIND LG-Mod wind estimates, as U/V components and speed/direction]</p> <p>Both REF_D and SAR_D are wind directions (expressed in degrees) with respect to the geographic North (0&deg;=North, 90&deg;=East, 180&deg;=South, 270&deg;=West), that the wind is blowing to.<br> Both REF_W and SAR_W are wind speeds (expressed in m/s).<br> Regarding REF_U/SAR_U and REF_V/SAR_V, note that a positive U component represents wind blowing to the East; a positive V component represents wind blowing to the North.</p> <p>13) SceneCentre_TrueHeading_FF; [Mean angle formed between the geographical South-North direction and the SAR azimuth direction (wrt the centre of the SAR Full-Frame image)]</p> <p>SceneCentre_TrueHeading_FF is a positive clockwise angle. In particular: SceneCentre_TrueHeading_FF is in ]180,360[ [deg].<br> Thus:<br> Descending Pass &lt;-&gt; &nbsp;SceneCentre_TrueHeading_FF is in ]180,270[ [deg]<br> Ascending Pass &nbsp;&lt;-&gt; &nbsp;SceneCentre_TrueHeading_FF is in ]270,360[ [deg]</p> <p>14) ROI_Npoints_UnUsablePointsMasked; [Number of samples used for each SARWIND LG-Mod wind estimation]</p> <p>15) MeanIncAng; 16) MeanNRCS; [Mean incident angle (expressed in degrees) and NRCS of the ROI]</p> <p>17) MeanResultantLength; 18) Alpha2_Est; [Fisher&#39;s formula parameters]</p> <p>19) MEdeg [Margin of Error, i.e. accuracy of each wind direction estimate, between 0&deg; and 45&deg;]</p> <p>The accuracy MEdeg is given by the semi-width of the confidence interval, with a confidence level (1-&alpha;) fixed, which is assigned to the wind direction estimate. Consequently, lower MEdeg values correspond to better estimates. And, if MEdeg == 45&deg;, wind estimates must be discharged.</p> <p><br> Finally, note also that you can cut an entire row when [SAR_U SAR_V SAR_W SAR_D] == [NaN NaN NaN NaN] (typically, this happens for &#39;land pixels&#39;).</p> <p>% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> %&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % REFERENCES: &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;%<br> % &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;%<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % The algorithm SARWIND LG-Mod is based on the Ph.D thesis below: &nbsp; &nbsp; &nbsp; &nbsp; %<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % [1] Rana, Fabio Michele (2016) &quot;Exploitation of Satellite &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % Synthetic Aperture Radar Data for Geophysical Parameters Retrieval over %<br> % Land and Ocean&quot;. Unpublished Ph.D thesis. Politecnico di Bari. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % Some applications of the method are described in the following papers: &nbsp;%<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % [2] Fabio M. Rana, Maria Adamo, Guido Pasquariello, Giacomo De Carolis, %<br> % and Sandra Morelli, &quot;LG-Mod: A Modified Local Gradient (LG) Method to &nbsp; %<br> % Retrieve SAR Sea Surface Wind Directions in Marine Coastal Areas,&quot; &nbsp; &nbsp; &nbsp;%<br> % Journal of Sensors, vol. 2016, Article ID 9565208, 7 pages, 2016. &nbsp; &nbsp; &nbsp; %<br> % doi:10.1155/2016/9565208. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % [3] Rana, F. M., Adamo, M., &amp; Blanda, P. (2018, July). &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % LG-Mod Multi-Scale Approach for Sar Sea Surface Wind Directions &nbsp; &nbsp; &nbsp; &nbsp; %<br> % Retrieval. In IGARSS 2018-2018 IEEE International Geoscience and Remote %<br> % Sensing Symposium (pp. 3216-3219). IEEE. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % [4] Rana, F. M., Adamo, M., Lucas, R., &amp; Blonda, P. (2019). Sea surface %<br> % wind retrieval in coastal areas by means of Sentinel-1 and numerical &nbsp; &nbsp;%<br> % weather prediction model data. Remote Sensing of Environment, 225, &nbsp; &nbsp; &nbsp;%<br> % 379-391. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % Suggestions and comments are always welcome. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % Thanks in advance, &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % Fabio Michele Rana &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % MOB: (+39) 3804114171 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % E-MAILS: fabiomichele.rana@gmail.com; &nbsp;fabiomichele.rana@iia.cnr.it &nbsp; &nbsp; %<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % SKYPE: fabiomichelerana &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % SARWIND_LG-Mod_v4.01, 2014-2019 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % Author: Fabio M. Rana &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> &nbsp;</p>

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Figure 2 in Harvestmen (Arachnida: Opiliones) from the Atlantic Forest of the Fernão Dias Environmental Protection Area, southern Minas Gerais, Brazil

Figure 2. Harvestmen records at the Fernão Dias EPA, Gonçalves municipality, southern Minas Gerais state: A) Ampheres luteus (Giltay, 1928). B) Megapachylus anomalus (Mello-Leitão, 1922). C) Gonyleptes pseudogranulatus (Soares, 1946). D) Munequita sp. / Registros de opiliones de la APA Fernão Dias, municipio de Gonçalves, sur del estado de Minas Gerais: A) Ampheres luteus (Giltay, 1928). B) Megapachylus anomalus (Mello-Leitão, 1922). C) Gonyleptes pseudogranulatus (Soares, 1946). D) Munequita sp.

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Figure 1 in Harvestmen (Arachnida: Opiliones) from the Atlantic Forest of the Fernão Dias Environmental Protection Area, southern Minas Gerais, Brazil

Figure 1. Sampling areas for harvestmen (Arachnida) in the Atlantic Forest of the Fernão Dias EPA in the municipality of Gonçalves, southern Minas Gerais state, in mixed and seasonal semideciduous forests. / Áreas de muestreo para opiliones (Arachnida) en la Mata Atlántica de la APA Fernão Dias en el municipio de Gonçalves, sur del estado de Minas Gerais, en bosques semideciduos mixtos y estacionales.

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Figure 1 in Gall-inducing insects from the Maricá Environmental Protection Area (RJ, Southeastern Brazil)

Figure 1 Insect galls from the Maricá Environmental Protected Area (Maricá, RJ): a) Lenticular leaf gall on Bignoniaceae sp.1, b) Linear leaf gall on Varronia curassavica Jacq. (Boraginaceae), c) Lenticular leaf gall on Protium brasiliense (Spr.) Engl. (Burseraceae), d-e) Galls on Andira fraxinifolia Benth. (Fabaceae): d) globoid, hairy leaf gall, e) Fusiform leaf vein gall, f) Fruit gall induced by Cecidomyiidae on Ocotea notata (Ness) Mez. (Lauraceae).

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Figure 4 in Gall-inducing insects from the Maricá Environmental Protection Area (RJ, Southeastern Brazil)

Figure 4 Insect galls from the Maricá Environmental Protected Area (Maricá, RJ): a) Fusiform stem gall on Smilax rufescens Griseb (Smilacaceae), b-c) Leaf galls on Lantana fucata Lindl. (Verbenaceae): b) Globoid, c) Cylindrical.

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Figure 3 in Gall-inducing insects from the Maricá Environmental Protection Area (RJ, Southeastern Brazil)

Figure 3 Insect galls from the Maricá Environmental Protected Area (Maricá, RJ): a) Lenticular leaf gall on Eugenia copacabanensis Kiaersk. (Myrtaceae), b) Flower bud gall on Neomitranthes obscura (DC.) N. J. E. Silveira (Myrtaceae), c-d) Leaf galls on Myrtaceae sp.1: c) Marginal roll, d) lenticular, e) Globoid, glabrous leaf gall on Guapira opposita (Vell.) Reitz (Nyctaginaceae), f) Fruit gall on Coccoloba sp. (Polygonaceae).

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Figure 6 in Gall-inducing insects from the Maricá Environmental Protection Area (RJ, Southeastern Brazil)

Figure 6 Grouping diagram (UPGMA) for similarity of gall-inducing insects among different Brazilian restingas. AC – Arraial do Cabo, ASP – Acaraí State Park, BAB – Babitonga, BRSP – Bertioga Restinga, State Park, CSSP – Costa do Sol State Park, FCPRNH – Fazenda Caruara Private Reserve of Natural Heritage, GR – Grumari Restinga, JRNP – Jurubatiba Restinga National Park, MEPA – Maricá Environmental Protection Area, MI – Marambaia Island, MR – Marambaia Restinga, PCVSP – Paulo César Vinha State Park, PSSBR – Praia do Sul State Biological Reserve.

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Figure 5 in Gall-inducing insects from the Maricá Environmental Protection Area (RJ, Southeastern Brazil)

Figure 5 Insect galls from the Maricá Environmental Protected Area (Maricá, RJ): a) Leaf galls on Neomitranthes obscura (DC.) N. Silveira (Myrtaceae) – black arrow: original galls induced by Stephomyia mina Maia, 1993 (Diptera, Cecidomyiidae), white arrows: galls modified by inquilines, b-c) Bud galls on Erythroxylum ovalifolium Peyr (Erythroxylaceae): b) Original gall induced by Lopesia erythroxyli Rodrigues &amp; Maia, 2010 (Diptera, Cecidomyiidae), c) Gall modified by inquilines, d-e) Bud galls on Eugenia astringens Cambess. (Myrtaceae): d) Original galls induced byStephomyia rotundifoliorum Maia, 1993 (Diptera, Cecidomyiidae), e) Galls modified by inquiline, f-g) Bud galls on Myrcia ovata Cambess. (Myrtaceae): f) Original gall induced by Myrciamyia maricaensis Maia, 1995 (Diptera, Cecidomyiidae), g) Gall modified by inquilines, h-j) Leaf galls on Paullinia weinmanniifolia Mart. (Sapindaceae): h) Original gall induced by Paulliniamyia ampla Maia, 2001 (Diptera, Cecidomyiidae), i) Gall modified by inquilines.

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Figure 2 in Gall-inducing insects from the Maricá Environmental Protection Area (RJ, Southeastern Brazil)

Figure 2 Insect galls from the Maricá Environmental Protected Area (Maricá, RJ): a) Fusiform stem gall on Struthanthus taubatensis Eichler (Loranthaceae), b-c) Flower bud galls on Byrsonima sericea DC. (Malpighiaceae): b) Ovoid, c) Cylindrical, d) Lenticular leaf gall on Schwartzia brasiliensis (Choisy) Bedell ex Gir.-Cañas (Marcgraviaceae), e) Globoid leaf gall on Marcetia taxifolia (A.St.-Hil.) DC. (Melastomataceae), f) Conical bud gall on Myrsine parvifolia (A.DC.) Mez. (Primulaceae).

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Figure 1 in Can wildlife mortality on a local road tell something general? An answer from a protected area in south-western Romania

Figure 1. Study area (blue line–rivers, black line–roads, discontinuous line–Iron Gates Natural Park limits, black dots–localities, red dots–the six studied sectors on the road to Bigăr).

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Figure 2 in Can wildlife mortality on a local road tell something general? An answer from a protected area in south-western Romania

Figure 2. The studied road in Sector 2 (up left) and Sector 3 (up right), and two road–killed vertebrates identified on the road: Salamandra salamandra (down left) and Talpa sp. (down right).

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Fig. 2 in Bat assemblages of protected areas in the state of Rio de Janeiro, Brazil

Fig. 2. Non-metric multidimensional scaling (NMDS) of the Bray-Curtis distance matrix, showing the dissimilarities between the strict nature reserves (P) and the sustainable-use protected areas (U) in the state of Rio de Janeiro, Brazil surveyed between 1989 and 2013.

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Fig. 1 in Bat assemblages of protected areas in the state of Rio de Janeiro, Brazil

Fig. 1. Protected areas in the state of Rio de Janeiro in which bat inventories have been conducted. The inset shows the location of Southeast Brazil in South America (the numbers correspond to those in Table III).

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Fig. 3 in Bat assemblages of protected areas in the state of Rio de Janeiro, Brazil

Fig. 3. Box plot of the first axis of the Non-Metric Multidimensional Scaling (NMDS1) representing the bat assemblages and different types of habitat of each protected area sampled in the state of Rio de Janeiro, Brazil between 1989 and 2013:1, Montane Forest; 2, Restinga; 3, Submontane Forest; 4, Pasture; 5, Secondary growth Vegetation; 6, Urban; 7, Eucalypt; 8, Uppermontane Forest. Each circle represents one of the protected areas sampled.

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Fig. 5 in Bat assemblages of protected areas in the state of Rio de Janeiro, Brazil

Fig. 5. Distribution of species captured in Protected Areas represented by the mean altitude categories of the sites surveyed in the state of Rio de Janeiro, Brazil between 1989 and 2013.

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Fig. 4 in Bat assemblages of protected areas in the state of Rio de Janeiro, Brazil

Fig. 4. Relationship between the geographic distance between each pair of protected areas and their Bray-Curtis dissimilarity in the quantitative composition of bat assemblages in the state of Rio de Janeiro, Brazil in surveys conducted between 1989 and 2013.

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Fig. 2 in Molecular detection of Cryptosporidium parvum in wild rodents (Phyllotis darwini) inhabiting protected and rural transitional areas in north-central Chile

Fig. 2. Phylogram representing analysis of the 18 rRNA region. The evolutionary history was inferred with maximum likelihood method and the Tamura 3-parameter (T92) model with a discrete Gamma distribution (5 categories (+G)). Analysis contains sequences uploaded from GenBank (with Cryptosporidium species, host, country, and accessions numbers in brackets) and those obtained in the present study are shown in triangles (with ID isolate, host, site of sampling and country, and accessions numbers in brackets). Bootstrap values are represented as per cent of internal branches (1000 replicates), and values lower than 50 are hidden. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. Cryptosporidium muris was used to root the tree.

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Fig. 1 in Molecular detection of Cryptosporidium parvum in wild rodents (Phyllotis darwini) inhabiting protected and rural transitional areas in north-central Chile

Fig. 1. Map of the Coquimbo region in Chile showing the two types of areas (i.e., Bosque Fray Jorge National Park - BFPNP [in green]; El Tangue Farm [in blue]) in which Darwin's leaf-eared mice (Phyllotis darwini) were sampled. In each area, 4 grids were established, and 200 capture points were allocated per grid. (UTM projection. Datum WGS84, Zone 19J).

opencc-by-4.0Aug 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record