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Habitat characteristics and species abundances of reptiles in northern Israel
<p>Data and auxiliary information collected in a field survey of reptile assemblages in northern Israel, conducted in the spring seasons from 2016 to 2018. Data were collected in 272 Mediterranean woodland and shrubland sites, exposed to different types of human land uses. Sites are located along a geo-climatic gradient of 120 km, with elevations between 50 m and 1,570 m above sea level. Species data contains the abundances of 15 lizards, six snakes and one tortoise. The species included in the data are:</p> <p><em>Mediodactylus orientalis, Ptyodactylus puiseuxi, Chamaeleo chamaeleon, Phoenicolacerta kulzeri, Phoenicolacerta laevis, Lacerta media, Ophisops elegans, Ablepharus rueppellii, Chalcides guentheri, Heremites vittate, Pseudopus apodus, Dolichophis jugularis, Hemorrhois nummifer, Platyceps collaris, Malpolon insignitus, Daboia palaestinae, Testudo graeca, Ptyodactylus guttatus, Laudakia stellio, Chalcides ocellatus, Eumeces schneideri, Psammophis schokari, </em>and<em> </em>unidentified lizard species.</p> <p>Data columns are:</p> <p>Sample = sample site code</p> <p>Date = sampling date</p> <p>Region = name of sub region in northern Israel</p> <p>Locality = sampling site name</p> <p>T_min = average minimum annual temperature from 1970 to 2000</p> <p>T_max = average maximum annual temperature from 1970 to 2000</p> <p>Precipitation = mean annual rainfal (mm) from 1970 to 2000</p> <p>Lat_sample = latitude of the centroid of the sampling site (m, ITM coordinate system)</p> <p>Lon_sample = longitude of the centroid of the sampling site (m, ITM cooridnate system)</p> <p>Elev_MEAN = elevation above sea level (m)</p> <p>EgrtPredPress = an estimate of predation pressure by cattle egrets (based on distance to nearest colony and colony size)</p> <p>Closeness = a visual estimate of vegetation cover</p> <p>Barrenness = a visual estimate of the amount of non-vegetated cover</p> <p>DISTURB = a visual estimate of the amount of anthropogenic disturbance to the site</p> <p>CATTLE = a visual estimate of cattle grazing pressure</p> <p>GOATS = a visual estimate of goat grazing pressure</p> <p>Shannon_LC = Shannon's index of habitat diversity</p> <p>Ndvi_MEAN = mean value of the normalized difference vegetation index captured by Landsat satellite imagery in August 2020</p> <p>Ndvi_STD = standard deviation of the values of the normalized difference vegetation index captured by Landsat satellite imagery in August 2020</p> <p>PreyLizAbun = total abundance of reptile species identified as cattle egret prey</p> <p>HerpAbun = total reptile abundance</p> <p>SpeciesRich = total species richness</p> <p>Columns 23 - 46: abundances of individual reptile species</p>
Habitat suitability predictions for a boreal forest indicator species, the northern goshawk (Accipiter gentilis), in Central Finland
<p>This repository contains files that show optimal sites in Central Finland for the northern goshawk (<em>Accipiter gentilis</em>, hereafter goshawk), an indicator species of boreal forests with conservation values. The optimal sites were derived from the habitat suitability model outputs included in the following publication:</p> <p> </p> <p><strong>Björklund Heidi<sup>a</sup>, Parkkinen Anssi<sup>b</sup>, Hakkari Tomi<sup>c</sup>, Heikkinen Risto K.<sup>d</sup>, Virkkala Raimo<sup>d</sup>, Lensu Anssi<sup>b</sup> (2020): Predicting valuable forest habitats using an indicator species for biodiversity. Biological Conservation, </strong><a href="https://doi.org/10.1016/j.biocon.2020.108682">https://doi.org/10.1016/j.biocon.2020.108682</a> . </p> <p> </p> <p><sup>a</sup> Finnish Museum of Natural History Luomus, P.O. Box 17, FI-00014 University of Helsinki, Finland</p> <p><sup>b</sup> University of Jyvaskyla, Department of Biological and Environmental Science, P.O. Box 35, FI-40014 University of Jyvaskyla, Finland</p> <p><sup>c</sup> Centre for Economic Development, Transport and the Environment Central Finland, P.O. Box 250, FI-40101 Jyväskylä, Finland</p> <p><sup>d</sup> Finnish Environment Institute, Biodiversity Centre, Latokartanonkaari 11, FI-00790 Helsinki, Finland</p> <p> </p> <p>The files are ArcGIS compatible shape files which indicate the spatial location of the 160 m × 160 m grid cells which include forest stands projected to be either highly suitable or suitable as a nesting site for the goshawk in Central Finland. The habitat suitability models and values were developed across the study area using Maxent software. The files show those 160-m grid cells from the study area which were included in one of the following two categories: (i) cells deemed as the most optimal (with high probability of suitable conditions) for goshawk nesting with suitability index values in Maxent outputs varying between 0.92–1.00 (‘best’ goshawk squares), and (ii) cells deemed as ‘good’ goshawk squares (with Maxent suitability index values of ≥ 0.69 and < 0.92). The coordinate system for the data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)). </p> <p>Summarization of the key settings and elements of the study are provided below. A detailed treatment can now be found in the article published in Biological Conservation (Björklund et al.) for which the link is the following: <a href="https://doi.org/10.1016/j.biocon.2020.108682">https://doi.org/10.1016/j.biocon.2020.108682</a> .</p> <p> </p> <p><strong>Summary of the study</strong></p> <p>Intensive commercial use of boreal forests is an accelerating threat to forest biodiversity, highlighting the development of cost-effective tools to detect the locations valuable for conservation. We applied species distribution models (SDMs) in our study area, Central Finland, to locate the optimal nesting sites for the goshawk, an indicator bird species for biodiversity hotspots in mature boreal forests. The optimal sites (here, 160 x 160 m grid squares) for the goshawk were determined using the Maxent software. Optimal squares for the goshawk had forests with considerably high volumes of Norway spruce (<em>Picea abies</em>, hereafter spruce) covering only 3.4% of the boreal landscape, and they were located mostly outside protected areas. Many of the squares with optimal nesting forests appeared to be under threat due to recently intensified logging operations. Half of the squares were logged to some extent and 10% were already lost or notably deteriorated due to logging after 2015 for which our models were calibrated. Threats to biodiversity of mature boreal spruce forests are likely to accelerate with increasing logging pressures. Thus, there is an urgent need to secure the continuous supply of mature spruce forests in the landscape by developing a denser network of protected areas and applying measures that aid in sparing large entities of mature forest on privately-owned land. Our modelled optimal squares can be used for selection of potential areas with biodiversity values in conservation prioritization.</p> <p><strong>The study species</strong></p> <p>The goshawk is a raptor species which prefers mature forests for nesting in Europe. Old forests dominated by spruce are considered as important for the breeding success of the species particularly in northern latitudes. Thus, intensive forest management can impair the breeding possibilities of the goshawk, and changes in forest landscapes are likely to contribute to the decline of the species. For example, in Finland, the goshawk is classified as nearly threatened species. In our study, we used the goshawk as an indicator species to model the spatial locations of boreal forest with much potential for including biodiversity values. The indicator species status of the goshawk is based on earlier studies showing the close association of the goshawk with various taxa of mature spruce forest, as well as the reported declines of both the goshawk and associated species due to loggings.</p> <p><strong>Developing Maxent models for the goshawk</strong></p> <p>The location data on occupied nests of the goshawk gathered in spring and summer 2015 and 2016 in Central Finland – as a part of the Finnish Common Birds of Prey Monitoring – were related to a set of environmental predictor variables using a maximum entropy method, Maxent software, which is considered particularly useful for modelling presence-only data (such as our goshawk nest site data). In our case, the data on forest stand and tree characteristics were related using Maxent to the known nesting sites to predict suitable conditions for the species across the Central Finland. The forest data used in the modelling were extracted from the multi-source national forest inventory (MS-NFI) data sources governed by the Natural Resources Institute Finland. The MS-NFI data used in our modelling are based on field data of the 11th and 12th NFIs from 2009 to 2016 and satellite images from 2015 and 2016.</p> <p>Prior modelling, Pearson correlations were calculated between the continuous environmental variables at the nest sites. Of the highly (|r| ≥ 0.7) correlated variables, we chose those variables which are known to be important for the goshawk, which are useful for generalization in other areas, or whose impact was of specific interest. Our final selected set of predictor variables included one class variable, site fertility class, and nine continuous variables: growing stock volume of the spruce, pine, birches and other hardwood, canopy cover, canopy cover of broad-leaved trees, saw timber of other broad-leaved trees than birches, pulpwood volume of the birches, and the biomass of the stem residual of the spruce. The original MS-NFI data recorded at the resolution of 16 × 16 m were resampled to the resolution of 160 × 160 m for the Maxent models, to represent one potential nesting forest stand.</p> <p>The accuracy of Maxent models were assessed with cross-validation and associated averaged AUC-values. The relative importance of the variables was measured by variable contribution and model deterioration measures provided by Maxent. The cloglog-transformed output index values ranging from 0 to 1 described the relative suitability of the 160-m squares to goshawk nesting. Based on the index values, the squares were classified as ‘optimal’ (with index values of 0.69–1.00), ‘typical’ (0.46– <0.69) and ‘poor’ (<0.46). In addition, we divided optimal squares into ‘best’ goshawk squares (index values of 0.92–1.00 corresponding to a high probability of suitable conditions), and ‘good’ goshawk squares (index values ≥ 0.69 and < 0.92).</p> <p><strong>Maxent model outputs</strong></p> <p>Spruce volume was the most important variable in defining habitat suitability for goshawk nesting, but hardwood cover, other hardwood logs and site fertility class contributed also to some extent to habitat suitability. In Maxent outputs, the set of 160-m squares deemed as optimal for goshawk nesting included 6 895 (cover 0.9% of the study area) best goshawk squares and 19 421 (cover 2.5%) good goshawk squares. The projected best and good goshawk squares were mostly located in unprotected areas: 95.0% of the best and 96.0% of the good goshawk squares occurred completely outside protected areas. For further details concerning the data and the model outputs, see the referred article Björklund et al. (2020).</p> <p><strong>State of the optimal goshawk squares</strong></p> <p>In total, 11% of best and over 9% of good goshawk squares were severely altered due to recent harvesting, typically clear-cutting, of the forests during the time period between 2015 and 2019. Altogether, some level of logging occurred in 3 062 (44%) of best goshawk and 9 846 (51%) of good goshawk squares during the recent years. However, many of the squares still included enough unlogged area for the goshawk in 2019.</p> <p>In our article, we conclude that while most of the optimal squares for the goshawk were still preserved in 2019, they are under risk as they are mainly situated outside protected area network. This stresses the importance of conserving biodiversity with complementary measures in privately-owned managed forests. In conclusion, a denser network with more PAs for forest-dwelling species should be secured in areas with intensive forestry, e.g. in southern Finland where PAs currently cover a smaller proportion of land compared to northern Finland.</p>
Wood density for 26 plant species collected from Northern Western Ghats
<p>This dataset contains wood density estimates for species collected from Sindhudurg district of Maharashtra. The data was collected for baseline data generation for the Sahyadri Restoration Program as part of CEROS Lab at Nature Conservation Foundation. The fieldwork was carried out in Feb-Mar 2024.</p> <p>Wood cores were collected using Increment Borer (Haglof 12inch, 3 thread, 5.15mm)</p> <p>Usage notes:</p> <p>readme_wood_density.txt contains the information for each columns and the values calculated</p> <p>Wood Density Maharashtra.csv contains the dataset</p> <p><strong>ACKNOWLEDGEMENTS:</strong></p> <p>I would like to thank GCPL CSR (Godrej Consumers Products) for funding this data collection as part of the Sahyadri Restoration project and S.P.K College Sawantwadi for providing the necessary lab support. Special thanks to Dr. Deelip Bharmal (Principal S.P.K College) and Dr. G.S Margaj (Professor Zoology Dept) for help with the lab analysis.</p>
Systematic revision of the Spotted and Northern Dusky Salamanders (Plethodontidae: Desmognathus conanti and D. fuscus), with six new species from the eastern United States
<p>Appendix S1 - Specimen, locality, and morphometric data for 674 individual salamanders analyzed in this study. We used the ‘geomorph’ package in R (Adams and Otárola-Castillo 2013) to extract 17 measurements at 0.01mm precision: SVL (snout-vent length), TL (tail length), AG (axilla-groin length), CW (chest width), FL (femur length), HL (humerus length), SG (snout-gular length), TW (tail width at rear of vent), TO (length of third toe), FI (length of third finger), HW (head width), ED (eye diameter), IN (internarial distance), ES (eye-snout distance), ON (orbito-narial distance), IO (inter-orbital distance), and IC (inter-canthal distance).</p>
Figures 1–9. Amphicoma spp. 1-3 in Amphicoma gandhara, a new species of Glaphyridae (Coleoptera: Scarabaeoidea) from Swat District in northern Pakistan
Figures 1–9. Amphicoma spp. 1-3) Amphicoma gandhara, new species, dorsal habitus. 1) Holotypus Ƌ. 2) Paratypus Ƌ#1. 3) Paratypus Ƌ#2. 4-6). Amphicoma gandhara, new species, parameres. 4) Right view. 5) Dorsal. 6) Left. 7-9) Amphicoma schneideri Nikodým, parameres. 7) Right view. 8) Dorsal. 9) Left.
Figure 20 in Erratum to Blahnik and Holzenthal (2017): Revision of the northern South American species of Mortoniella Ulmer, 1906 (Trichoptera: Glossosomatidae: Protoptilinae)
Figure 20. Mortoniella (Mortoniella) tanyrhabdos, new species, male genitalia. A—lateral; B—segment IX and tergum X, dorsal; C—phallic ensemble, ventral; D—ventral process of segment VI, lateral.
Figure 19 in Erratum to Blahnik and Holzenthal (2017): Revision of the northern South American species of Mortoniella Ulmer, 1906 (Trichoptera: Glossosomatidae: Protoptilinae)
Figure 19. Mortoniella (Mortoniella) flinti Sykora, male genitalia. A—lateral; B—segment IX and tergum X, dorsal; C—phallic ensemble, ventral; D—dorsal phallic spine, dorsal; E—ventral process of segment VI, lateral.
Figure 21 in Erratum to Blahnik and Holzenthal (2017): Revision of the northern South American species of Mortoniella Ulmer, 1906 (Trichoptera: Glossosomatidae: Protoptilinae)
Figure 21. Mortoniella (Mortoniella) tusci, new species, male genitalia. A—lateral; B—segment IX and tergum X, dorsal; C—phallic ensemble, ventral; D—dorsal phallic spine, dorsal; E—ventral process of segment VI, lateral.
Figure 70 in Erratum to Blahnik and Holzenthal (2017): Revision of the northern South American species of Mortoniella Ulmer, 1906 (Trichoptera: Glossosomatidae: Protoptilinae)
Figure 70. Mortoniella (Mortoniella) croca, new species, male genitalia. A—lateral; B—segment IX and tergum X, dorsal; C—phallic ensemble, ventral; D—dorsal phallic spine, dorsal; E—ventral process of segment VI, lateral.
Figure 50 in Erratum to Blahnik and Holzenthal (2017): Revision of the northern South American species of Mortoniella Ulmer, 1906 (Trichoptera: Glossosomatidae: Protoptilinae)
Figure 50. Mortoniella (Mortoniella) gracilis, new species, male genitalia. A—lateral; B—segment IX and tergum X, dorsal; C—phallic ensemble, ventral.
Figures 128-137 in Erratum to Blahnik and Holzenthal (2017): Revision of the northern South American species of Mortoniella Ulmer, 1906 (Trichoptera: Glossosomatidae: Protoptilinae)
Figures 128-137. Character evolution in Protoptila/Mortoniella male genitalia, lateral. 128—Protoptila trichoglossa Blahnik and Holzenthal. 129—Mortoniella (Nanotrichia) rodmani Blahnik and Holzenthal (ormina grp.). 130—M. (Nanotrichia) Ʋenezuelensis n. sp. (velasquezi grp.) 131—M. (Nanotrichia) macarenica Flint (ormina grp.). 132—M. (Mortoniella) unilineata Sykora (unplaced to species grp.). 133—M. (Mortoniella) cornuta n. sp. (unplaced to species grp.). 134—M. (Mortoniella) proakantha n. sp. (unplaced to species grp.). 135—M. (Mortoniella) catherinae n. sp. (bilineata grp.). 136—M. (Mortoniella) akantha Blahnik and Holzenthal (leroda grp.). 137—M. (Mortoniella) simla Flint (leroda grp.).
Figures 1–6. Passadena mistralae n in Contribution to the knowledge of Chilean Phycitinae (Lepidoptera: Pyralidae): new species of Passadena Hulst, 1900, and Ragonotia Grote, 1888, from northern Chile
Figures 1–6. Passadena mistralae n. sp. habitus. 1) Holotype female. 2–3) Female paratypes. 4) Male paratype. 5) Head of female. 6) Head of male. Scale: 1.0 mm.
Fig. 8 in A new species of Anadia (Reptilia, Squamata) from the Venezuelan 'Lost World', northern South America
Fig. 8. Map of the Guiana Shield region (A), with enlarged black rectangle corresponding to the Chimantá Massif (B). Black triangle indicates type locality, black dots indicate other know localities for the species.
Fig. 7 in A new species of Anadia (Reptilia, Squamata) from the Venezuelan 'Lost World', northern South America
Fig. 7. Anadia mcdiarmidi sp. nov. Intrapopulation variation in the condition of the parietals and the interparietal (frontoparietals in grey). Top, from left to right: IRSNB 2677, IRSNB 2676; bottom, from left to right: IRSNB 2675, IRSNB 2674. Drawings not to scale.
Fig. 6 in A new species of Anadia (Reptilia, Squamata) from the Venezuelan 'Lost World', northern South America
Fig. 6. Anadia mcdiarmidi sp. nov. Intrapopulation variation in dorsal (above) and ventral (below) colour pattern in preserved specimens. From left to right: IRSNB 2677, IRSNB 2674, IRSNB 2676, IRSNB 2675. Photographs by Philippe J.R. Kok.
Fig. 1 in A new species of Anadia (Reptilia, Squamata) from the Venezuelan 'Lost World', northern South America
Fig. 1. Anadia mcdiarmidi sp. nov. A. IRSNB 2677, ♂ holotype in life. B. IRSNB 2675, juvenile male paratype in life. C. Ventral view of the anaesthetized holotype (grid squares = 5 mm). D. Summit of Abakapá-tepui, looking NW, showing the macrohabitat of the new species (03-05-2011). Photographs by Philippe J.R. Kok.
Fig. 3 in A new species of Anadia (Reptilia, Squamata) from the Venezuelan 'Lost World', northern South America
Fig. 3. Anadia mcdiarmidi sp. nov. Subpelvic region of the preserved ♂ holotype (IRSNB 2677), showing preanal scales and femoral pores, and trunk of the preserved holotype in lateral view (head to left), showing scalation at midbody. Scale lines = 5 mm. Photographs by Philippe J.R. Kok.
Figure 4. Skelosophusa prolixa. A dorsal view B frontal view C sternal view D in A new species of Foza Reed & Cumberlidge, 2006 from northern Madagascar (Decapoda, Brachyura, Potamoidea, Potamonautidae), with a redescription of F. goudoti (H. Milne Edwards, 1853) comb. n., and comments on Skelosophusa prolixa Ng & Takeda, 1994
Figure 4. Skelosophusa prolixa. A dorsal view B frontal view C sternal view D major (left) cheliped. Adult male (NHM 2009.122), CW 29.3 mm.
Figure 2 in A new species of Foza Reed & Cumberlidge, 2006 from northern Madagascar (Decapoda, Brachyura, Potamoidea, Potamonautidae), with a redescription of F. goudoti (H. Milne Edwards, 1853) comb. n., and comments on Skelosophusa prolixa Ng & Takeda, 1994
Figure 2. Foza ambohitra sp. n. A dorsal view B frontal view C sternal view D major (right) cheliped. A, B adult female (FMNH 11056), CW 43.1 mm C, D holotype, adult male, CW 39.5 mm.
Fig. 3 in New collections of freshwater crabs from northern Madagascar, with the description of a new species of Foza Reed & Cumberlidge, 2006 (Brachyura, Potamonautidae), and comments on their conservation status
Fig. 3. Foza manonae sp. nov. Holotype, adult ♂, CW 43.7 mm, Ankarana Special Reserve, Madagascar (ZSM A20145003). Entire animal. A. Dorsal view. B. Frontal view. C. Sternal view. Scale bar: 14.6 mm.
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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.