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Potential forest conservation value rasters for Denmark from Assmann et al. "LiDAR data fusion and machine learning identify temperate forests of high conservation value"
<p>Potential forest conservation value (high / low) rasters for Denmark based on a remote sensing data fusion approach. Please see manuscript (below) for a detailed description of the methods and data products. </p> <p><br>Jakob J. Assmann, Pil B. M. Pedersen, Jesper E. Moeslund, Cornelius Senf, Urs A. Treier, Derek Corcoran, Zsófia Koma, Thomas Nord-Larsen, Signe Normand. In prep. LiDAR data fusion and machine learning identify temperate forests of high conservation value.</p> <p><br>When using the data, please cite the above manuscript. </p> <p><br>Files description:</p> <ul> <li>Compressed and cloud optimised rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:3857 <ul> <li>forest_quality_ranger_biowide_10m_cog_epsg3857.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m_cog_epsg3857.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m_cog_epsg3857.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m_cog_epsg3857.tif GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul> <p> </p> <ul> <li>Aggregated rasters of potential forest conservation value projections for Denmark (100 m res.) in EPSG:25832 <ul> <li>forest_quality_ranger_biowide_100m.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_100m.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_100m.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_100m.tif GBM model projections based on SustainScapes stratification </li> </ul> </li> </ul> <p> </p> <ul> <li>Uncompressed and tiled rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:25832<br>Please note: the archives contain approx. 42k tiles, each 10 x 10 km, as well as a VRT file for covenient loading. <ul> <li>forest_quality_ranger_biowide_10m.zip RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m.zip RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m.zip GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m.zip GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul>
Datapackage for national high-resolution conservation prioritisation of boreal forests
<p>This data package concerns the following work:</p> <p>Ninni Mikkonen, Niko Leikola, Joona Lehtomäki, Panu Halme, Atte Moilanen,<br> National high-resolution conservation prioritisation of boreal forests,<br> Forest Ecology and Management, Volume 541, 2023, 121079<br> ISSN 0378-1127</p> <p><a href="https://doi.org/10.1016/j.foreco.2023.121079">https://doi.org/10.1016/j.foreco.2023.121079</a></p> <p>The overall objective of the work was to develop spatial prioritizations that can assist the forest conservation programme METSO (The Finnish Government 2008; 2014) to make well-informed decisions about acquisition of forests for protection. The results are also aimed to be useful for other actors interested in forest conservation or biodiversity friendly forest management. We focused the prioritization on the most threatened forest types and areas that display some or many elements of natural forests: more than one and preferably more than two tree species, forest structure that present else than even age structure or a history of clear-cut harvesting, and the amount of dead wood that exceed the volume of dead tree material in managed forests. From the perspective of connectivity, these areas should be situated close (varying from metres to a few kilometres) to other valuable forest areas. These kinds of forest areas represent the most threatened forest types and forest species in Finland (Hyvärinen et al. 2019; Kontula and Raunio 2019).</p> <p>This data package includes 3 folders (see details of the data in the article):</p> <p>1) Data folder<br> a) DWP features: the 20 input data layers of the modelled biodiversity surrogate: dead wood potential. These are combinations of 4 tree species and 5 forest site type classes. Not that these are not normalized.<br> (1) bir = birch, obl = other broad leaved tree, st = forest site type<br> b) Other data layers:<br> i) condition_layer.img where the magnitude of the penalty is defined<br> ii) ProtectedOrNot.img layer is used in hierarchical analysis to define whether the area is permanently protected or not<br> iii) WRSCR04_PA.img layer consists of permanently protected areas cut form weighted range size corrected richness output layer from analysis version 4 to execute the positive interaction between the forests and permanently protected areas.<br> iv) similarity matrix</p> <p>2) Input folder<br> a) example setup files for analysis version 7 (hierarchical analysis where permanently protected areas are forced to highest priorities, including information on dead wood potential of the forest stands, penalties followed by the forest management, connectivity within the forests, observations of red-listed forest species, and connectivity to forest key habitats and permanently protected areas)<br> i) .spp file for list of input features for the analysis<br> ii) .dat file for the analysis settings<br> iii) .bat file to run the analysis in command line<br> iv) conditionlayer.txt to define the used condition file in the analysis<br> v) groups file to define the use of the condition layer<br> vi) interact file to define the interactions between feature layers in connectivity calculations</p> <p>3) Output folder<br> a) includes folder for each analysis version. Each folder includes<br> i) rank file in .img format which is the actual spatial priority ranking result<br> ii) wrscr file which describes the weighted range size corrected richness of all input features<br> iii) curves file: the performance of each input feature within the cell removal<br> iv) jpg picture of the result</p> <p>The package DOES NOT include sensitive data. For species observations, ask for Finnish Biodiversity Info Facility https://laji.fi/en. For forest key habitats (small forest patches protected by the Forest Act, that are classified as “habitats of special importance to safeguard the biodiversity of forests”) on state owned land and land owned by companies, ask the data providers and owners.</p> <p>See Moilanen et al. (2014) for more technical information on the input and output files.</p> <p><br> Overview of the data</p> <p>The resolution of the spatial data is 96 m x 96 m. The study area covered the forested land area in Finland, excluding the autonomous Åland Islands.</p> <p>The data on forest stands are from year 2015, the forest management year 2017, and protected area network early winter 2018. See details of the data extraction in the article, Appendix A.</p> <p>The main source of biodiversity information were the modelled dead wood potential (DWP) indices. The DWP is an estimation of the potential of a stand for hosting dead wood dependent species. The potential is increased when the stand can be expected to produce more dead wood and more varied dead wood in terms of size and tree species composition. The modelling is based on forest growth and increase of dead wood calculated with Motti forest simulator 3.3 (Salminen et al., 2005; Hynynen et al., 2014; Hynynen et al., 2015) for 168 combinations of seven tree species, six forest site types, and four vegetation zones. See detailed information on the dead wood potential modelling in doi:10.3390/f11090913 (Mikkonen et al. 2020, Modeling of Dead Wood Potential Based on Tree Stand Data)</p> <p>The DWP was calculated for each stand or pixel based on the forest data (Finnish Forest Centre 2015; Metsähallitus 2015; Metsähallitus Parks & Wildlife Finland and Centres for Economic Development Transport and the Environment 2015; Natural Resources Institute Finland 2015b; 2015a): tree species and tree stock quantities (mean diameter at breast height and volume), soil fertility (Cajander, 1926), and location. In the DWP modelling the size information was combined with stand volume and forest site type. Eventually, the data were compiled to 20 input layers. See detailed information on the pre-processing of the input-data in the Appendix B.</p> <p>Spatial conservation prioritizations were made with the Zonation software 4.0 (Moilanen et al. 2005; Moilanen et al. 2009; Moilanen et al. 2011). With multiple analysis versions, the greatest interest is on those areas that repeatedly receive high ranks – these areas are important from all perspectives included in analysis.</p> <p>The ecological model of conservation value included seven analysis versions that start from a local perspective and then evolve towards regional and national levels (following Lehtomäki et al. 2009). Each new analysis version included everything that had been included in the previous simpler versions. The versions are 1) local estimation of the conservation potential of the forests based on tree stock alone, 2) local estimation with additional information about forest management and drainage, 3) landscape level (not local but not regional either) estimation with internal forest connectivity, 4) landscape level estimation with additional information about observations of red-listed forest species, 5) landscape-level estimation with added short distance connectivity to key forest habitats, 6) regional estimation with added long distance connectivity to permanently protected areas, and 7) regional estimation of the most appropriate addition to the present conservation network.</p> <p>These results do not replace in-depth ecological inventory assessment. They can be used as one source of information in land use planning.</p> <p><br> Literature</p> <p>Finnish Forest Centre. 2015. [dataset] Field and forest stand database AARNI.</p> <p>Hyvärinen, E., Juslén, A., Kemppainen, E., Uddström, A. & Liukko, U.-M. (Eds.). 2019. The 2019 Red List of Finnish Species. Helsinki, Ministry of the Environment & Finnish Environment Institute. 704 p.</p> <p>Kontula, T. & Raunio, A. (Eds.). 2019. Threatened Habitat Types in Finland 2018. Red List of Habitats – Results and Basis for Assessment. Helsinki, Finnish Environment Institute and Ministry of the Environment. The Finnish Environment 2/2019. 254 p. http://urn.fi/URN:ISBN:978-952-11-5110-1<br> http://hdl.handle.net/10138/308426.</p> <p>Lehtomäki, J., Tomppo, E., Kuokkanen, P., Hanski, I. & Moilanen, A. 2009. Applying spatial conservation prioritization software and high-resolution GIS data to a national-scale study in forest conservation. Forest Ecology and Management 258(11): 2439-2449.</p> <p>Metsähallitus. 2015. [dataset] SutiGIS 2015. Forestry resource and planning system for Metsähallitus Forestry Ltd and Protected Area Biotope Information System; biotope, and tree stock data on state-owned conservation areas, for Metsähallitus Parks & Wildlife Finland.</p> <p>Metsähallitus Parks & Wildlife Finland & Centres for Economic Development Transport and the Environment. 2015. [dataset] SutiGIS 2015: Protected area biotope information system, biotope and tree stock data on private conservation areas.</p> <p>Mikkonen, N., Leikola, N., Lehtomäki, J., Halme, P. & Moilanen, A. 2023. National high-resolution conservation prioritisation of boreal forests. Forest Ecology and Management, Volume 541. <a href="https://doi.org/10.1016/j.foreco.2023.121079">https://doi.org/10.1016/j.foreco.2023.121079</a></p> <p>Mikkonen, N., Leikola, N., Halme, P., Heinaro, E., Lahtinen, A. & Tanhuanpää, T. 2020. Modeling of Dead Wood Potential Based on Tree Stand Data. Forests 11(913): 21.</p> <p>Moilanen, A., Franco, A. M. A., Early, R. I., Fox, R., Wintle, B. & Thomas, C. D. 2005. Prioritizing multiple-use landscapes for conservation: methods for large multi-species planning problems. Proceedings of the Royal Society B-Biological Sciences 272(1575): 1885-1891.</p> <p>Moilanen, A., Kujala, H. & Leathwick, J. 2009. The Zonation framework and software for conservation prioritization. In: Moilanen, A., Wilson, K. A. & Possingham, H. P. (Eds.). Spatial conservation prioritization - Quantitative Methods & Computational tools. New York, Oxford University Press Inc. p. 196-210.</p> <p>Moilanen, A., Leathwick, J. R. & Quinn, J. M. 2011. Spatial prioritization of conservation management. Conservation Letters 4(5): 383-393.</p> <p>Moilanen, A., Pouzols, F. M., Meller, L., Veach, V., Arponen, A., Leppänen, J. & Kujala, H. 2014. Zonation - Spatial conservation planning methods and software. Version 4. User Manual. 4. Helsinki, C-BIG Conservation Biology, Informatics Group, Department of Biosciences, University of Helsinki, Finland. 290 p.</p> <p>Natural Resources Institute Finland. 2015a. [dataset] Segmented multi-source national forest inventory data of Finland: estimates of mean diameter at breast height for tree species based on National Forest Inventory 2013. Unpublished. Date of datacut 19.8.2015.</p> <p>Natural Resources Institute Finland. 2015b. [dataset] The Multi-Source National Forest Inventory of Finland (MS-NFI) 2013, CC BY 4.0.</p> <p>The Finnish Government. 2008. Decision-in-Principle of The Finnish Government on the Forest Biodiversity Programme for Southern Finland for years 2008-2016 (in Finnish). 13.</p> <p>The Finnish Government. 2014. Decision-in-Principle of the Finnish Government on extension of the Forest Biodiversity Programme for Southern Finland (METSO) for years 2014-2025. 18.</p> <p> </p>
Data from: Habitat selection in transformed landscapes and the role of forest remnants and shade coffee in the conservation of resident birds
1. Biodiversity conservation in transformed landscapes is becoming increasingly important. However, most assessments of the value of modified habitats rely heavily on species presence and/or abundance, masking ecological processes such as habitat selection and phenomena like ecological traps, which may render species persistence uncertain. High species richness has been documented in tropical agroforestry systems but comparisons with native habitat remnants generally lack detailed information on species demography and habitat use. 2. We generated a multi-species, multi-measure framework to evaluate the role of habitat selection in the adaptation of species to transformed landscapes, and demonstrate that its use could affect how we value the contribution different land uses make to biodiversity conservation. 3. We analyzed seven years of capture-mark-recapture and observation data for twelve species of resident birds present in native forest remnants and shade coffee plantations in a mega-diverse region. We assessed whether species behaved adaptively by evaluating the correlation between measures of habitat preference (occurrence, abundance, fidelity, inter-seasonal variance and age) and performance (body condition, muscle, primary molt, breeding and juveniles) in forest and coffee, and generated hypotheses about their role in species persistence. 4. We documented adaptive habitat selection for seven species, non-ideal selection for four, and maladaptive selection for one. While many species showed equal-preference and/or equal performance in many traits, in general we found more evidence for birds preferring and/or performing better in forest than coffee, although relationships between our indicators and population adaptation need to be studied further before our proposed framework can be applied to more species and landscapes. 5. While shade coffee can act as a biodiversity-friendly matrix providing complementary or supplementary habitat to a wide range of resident bird species, protecting remnants of native vegetation is still of paramount importance for biodiversity conservation in agricultural landscapes. 28-Aug-2019
Fig. 1 in Natural-Licks Use By Orangutans And Conservation Of Their Habitats In Bornean Tropical Production Forest
Fig. 1. Location maps of Deramakot Forest Reserve in Sabah, Malaysian Borneo (D1 to D4: natural-licks).
Figure 3 in Conservation of Manilkara salzmannii in an Atlantic Forest fragment through the study of fruits and seeds
Figure 3. Absolute frequency distribution of the biometric characteristics of the fruits and seeds of Manilkara salzmannii.
Figura 2. Manilkara salzmannii. A in Conservation of Manilkara salzmannii in an Atlantic Forest fragment through the study of fruits and seeds
Figura 2. Manilkara salzmannii. A. tree in natural forest; B. mature fruits collected; C. mature fruit size (scale = 3cm); D. seed size (scale = 1 cm).
Figure 1 in Conservation of Manilkara salzmannii in an Atlantic Forest fragment through the study of fruits and seeds
Figure 1. Geographical location of the study area. A. Municipality of Macaíba, RN, Brazil; B. fragment with a population of Manilkara salzmannii.
The conservation burden of Intact Forest Landscapes (IFLs): A global database of management units and IFLs
<p><strong>Introduction</strong></p> <p>This dataset includes a global overview of publicly available forest Management Units (MUs), Intact Forest Landscapes (IFLs) and their overlap. This includes both the boreal forests of Canada and Russia, and the tropical forests in the Amazon, the Congo basin, South-East Asia. The dataset was developed for the paper "Feasibility and effectiveness of global Intact Forest Landscape protection through forest certification: The conservation burden of Intact Forest Landscapes" by Zwerts et al. (2024). A comprehensive list of MUs with % and absolute overlap with IFLs is presented in Table S1 of Zwerts et al. (2024).</p> <p><strong>Data collection</strong></p> <p>We collected and collated all publicly available MU and IFL data of Central Africa, Southeast Asia, the Amazon, and of the boreal forests in Canada and Russia. As such, we included MU data from Cameroon, Canada, the Central African Republic, the Democratic Republic of Congo, Equatorial Guinea, Gabon, Indonesia, Malaysia, the Republic of Congo and Russia. Together, these forests comprise the majority of all IFLs (Potapov et al., 2017). We utilized the 2020 intact forest landscape (IFL) dataset generated by Potapov et al. (2017). Both FSC-certified and non-FSC MUs were considered and FSC-certification status data was collected using the FSC public dashboard (FSC, 2023). All data was collected in March 2023. Our dataset is not exhaustive. To our knowledge, not all MU data is publicly available. For Southeast Asia no public MU data is available for Papua New Guinea and Peninsular Malaysia. For the Amazon, insufficient public MU data was available to create an accurate representation of the situation. This area was excluded from the main analysis in Zwerts et al., 2024. We included a distinction between FSC-certified and non-FSC MUs in Russia, even though the FSC has withdrawn all certificates in Russia in April 2023 following the invasion of Ukraine. We chose to retain the distinction between FSC and non-FSC MUs for the Russian data because of the uncertainty of the current situation and the significant influence of FSC-certification in the Russian management of IFLs.</p> <p><strong>Overlap analysis</strong></p> <p>All area was transformed to geodesic distance. Furthermore, several MU names were altered because of duplicate names. The number of hectares of MUs that overlap with IFLs was calculated in ArcGIS Pro 3.0.0, using the WGS_1984_Web_Mercator_Auxiliary_Sphere coordinate system. Using the intersect and multipart to singlepart tools every overlap fragment was isolated. For the results in Zwerts et al. (2024) the total overlap and the percentage of overlap was calculated in R. </p> <p><strong>Abstract of the related article</strong></p> <p>Intact Forest Landscapes (IFLs) are defined as forested areas of at least 500 km2 that show no signs of remotely sensed human activity. They are considered to be of high conservation value due to their role in maintaining biodiversity and mitigating climate change. In 2014, the members of the Forest Stewardship Council (FSC), one of the major global certification schemes for responsible forest management, took a conservation stand by restricting logging in FSC-certified IFLs. However, this move raised concerns about the economic viability of FSC-certified logging in these areas. To address these challenges, in 2022, FSC proposed an integrated landscape approach, considering local conditions and stakeholders' needs to balance IFL protection, economic sustainability, and community interests. Here, we leverage publicly available management unit (MU) data, to provide a global quantitative overview of IFLs designated for timber production. We use the concept of 'conservation burden' for the extent that MUs overlap with IFLs, representing the impact that IFL protection has on forest management operations if logging is disallowed. Our data indicates that currently FSC-certified MUs affect 0.6% of global IFLs. Too restrictive policies for logging in IFLs may discourage FSC-certification in global IFLs. Considering the environmental and social benefits of FSC certification, it warrants careful examination whether the benefits of protecting a limited subset of FSC-certified IFLs outweighs the cost of potentially reduced growth of the total FSC-certified area. Our data can provide a basis to facilitate stakeholder engagement for landscape-level IFL management.</p>
Data from: The extreme rainfall gradient of the Cape Horn Biosphere Reserve and its impact on forest bird richness. Biodiversity and Conservation
<p><strong>Description of dataset</strong></p> <p>This dataset contains information about forest bird species richness and climatic variables in 61 sample sites of the Cape Horn Biosphere Reserve. This dataset was analysed in : Quilodrán CS, Sandvig EM, Aguirre F, Rivero de Aguilar J, Barroso O, Vásquez RA, and R Rozzi. 2022. Effects of the extreme rainfall gradient in the Cape Horn Biosphere Reserve on forest bird richness. <em>Biodiversity and Conservation</em>. </p> <p> </p> <p><strong>Acknowledgments </strong></p> <p>This study was funded by grants for Technological Centers of Excellence with Basal Financing of the National Agency for Research and Development (ANID-Chile), granted to the Cape Horn International Center (CHIC- FB210018) and the Institute of Ecology and Biodiversity (IEB-AFB170008). CSQ acknowledges support from the Swiss National Science Foundation (N°P5R5PB_203169). </p>
Maps and R code from: Marginality indices for biodiversity conservation in forest trees
<p>This dataset provides the raster map of marginality indices for eight European tree species as described in the article "Marginality indices for biodiversity conservation in forest tree". It also provided the R code to compute the marginality indices.</p> <p>The eight species are the following:</p> <ul> <li><em>Abies alba</em> Mill. (silver fir)</li> <li><em>Fagus sylvatica</em> L. (European beech)</li> <li><em>Picea abies</em> (L.) H.Karst. (Norway spruce) </li> <li><em>Pinus halepensis</em> Mill. (Aleppo pine)</li> <li><em>Pinus nigra</em> J.F.Arnold (black pine)</li> <li><em>Pinus pinaster</em> Aiton (maritime pine) </li> <li><em>Pinus pinea</em> L. (stone pine)</li> <li><em>Pinus sylvestris</em> L. (Scots pine)</li> </ul> <p>For each species, a 7z archive of a multi-layered image of the raster maps of the marginality indices is given. Each image has 12 layers:</p> <ul> <li>Layer 1: distribution map of the species</li> <li>Layer 2: map of the probability of being marginal according to the Maxent model using eight marginality indices and countries as predictors</li> <li>Layer 3: map of the environmental marginality index, defined as the z-transform of the suitability of each location as predicted by a species distribution model using climatic variables as predictors</li> <li>Layer 4: map of the segmented distribution according to morphological spatial pattern analysis</li> <li>Layer 5: map of the area index</li> <li>Layer 6: map of the gravity index</li> <li>Layer 7: map of the centroid index</li> <li>Layer 8: map of the edge index</li> <li>Layer 9: map of the isolation index</li> <li>Layer 10: map of the second nearest core index</li> <li>Layer 11: map of the north/south index</li> <li>Layer 12: map of the east/west index</li> </ul>
FIGURE 15 in The herpetofauna of the Serra do Urubu mountain range: a key biodiversity area for conservation in the brazilian atlantic forest
FIGURE 15: Sample rarefaction curve for lizards and amphisbaenids in RPPN Pedra D'Antas, after 24 days of effort.
FIGURE 3 in The herpetofauna of the Serra do Urubu mountain range: a key biodiversity area for conservation in the brazilian atlantic forest
FIGURE 3: Amphibians species recorded at the Serra do Urubu mountain range. (A) Dendropsophus haddadi, (B) Dendropsophus minutus, (C) Dendropsophus oliveirai, (D) Dendropsophus soaresi, (E) Boana albomarginata, (F) Boana atlantica, (G) Boana crepitans, (H) Boana exastis.
FIGURE 11 in The herpetofauna of the Serra do Urubu mountain range: a key biodiversity area for conservation in the brazilian atlantic forest
FIGURE 11: Reptile species recorded at the Serra do Urubu mountain range. (A) Salvator merianae, (B) Strobilurus torquatus (Photo by C.O. Gussoni), (C) Tropidurus hispidus, (D) Tropidurus semitaeniatus (Photo by C.O. Gussoni), (E) Boa constrictor, (F) Corallus hortulanus, (G) Epicrates assisi, (H) Spilotes pullatus.
FIGURE 10 in The herpetofauna of the Serra do Urubu mountain range: a key biodiversity area for conservation in the brazilian atlantic forest
FIGURE 10: Reptile species recorded at the Serra do Urubu mountain range. (A) Dryadosaura nordestina, (B) Iguana iguana, (C) Enyalius aff. catenatus (fêmea), (D) Enyalius aff. catenatus (macho), (E) Gymnodactylus darwinii, (F) Polychrus marmoratus, (G) Mabuya nigropunctata, (H) Ameiva ameiva (Photo by C.O. Gussoni).
FIGURE 6 in The herpetofauna of the Serra do Urubu mountain range: a key biodiversity area for conservation in the brazilian atlantic forest
FIGURE 6: Amphibians species recorded at Serra do Urubu mountain range. (A) Leptodactylus fuscus, (B) Leptodactylus cf. latrans, (C) Leptodactylus natalensis, (D) Leptodactylus troglodytes, (E) Leptodactylus vastus, (F) Physalaemus cuvieri, (G) Pseudopaludicola mystacalis, (H) Chiasmocleis alagoana.
FIGURE 2 in The herpetofauna of the Serra do Urubu mountain range: a key biodiversity area for conservation in the brazilian atlantic forest
FIGURE 2: Amphibians species recorded at the Serra do Urubu mountain range. (A) Rhinella crucifer, (B) Rhinella granulosa (Photo by C.O. Gussoni), (C) Rhinella jimi (Photo by C.O. Gussoni), (D) Gastrotheca fissipes, (E) Gastrotheca pulchra (Photo by B. Lisboa), (F) Hylomantis granulosa, (G) Dendropsophus branneri, (H) Dendropsophus elegans.
FIGURE 1 in The herpetofauna of the Serra do Urubu mountain range: a key biodiversity area for conservation in the brazilian atlantic forest
FIGURE 1: Map of RPPN Pedra D'Antas and RPPN Frei Caneca, at the Serra do Urubu mountain range municipalities of Jaqueira and Lagoa dos Gatos, Pernambuco State, Brazil, with the respectives study sites.
FIGURE 14 in The herpetofauna of the Serra do Urubu mountain range: a key biodiversity area for conservation in the brazilian atlantic forest
FIGURE 14: Reptile species recorded at the Serra do Urubu mountain range. (A) Amerotyphlops arenensis, (B) Crotalus durissus, (C) Lachesis muta.
FIGURE 13 in The herpetofauna of the Serra do Urubu mountain range: a key biodiversity area for conservation in the brazilian atlantic forest
FIGURE 13: Reptile species recorded at the Serra do Urubu mountain range. (A) Philodryas olfersii (Photo by C.O. Gussoni), (B) Pseudoboa nigra, (C) Sibynomorphus sp. (D) Taeniophallus affinis, (E) Xenodon sp. (Photo by C.O. Gussoni), (F) Xenopholis scalaris, (G) Micrurus sp. (H) Micrurus lemniscatus carvalhoi.
FIGURE 9 in The herpetofauna of the Serra do Urubu mountain range: a key biodiversity area for conservation in the brazilian atlantic forest
FIGURE 9: Reptile species recorded at the Serra do Urubu mountain range. (A) Amphisbaena alba, (B) Amphisbaena pretrei, (C) Norops fuscoauratus, (D) Dactyloa punctata, (E) Diploglossus lessonae (juvenile), (F) Diploglossus lessonae (adult), (G) Ophiodes sp. (H) Hemidactylus mabouia (Photo by C.O. Gussoni).
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.