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7,081 results for “Habitats”
Hubbard Brook Wildlife Monitoring Project: Assessing wildlife population presence, activity and habitat use through continual camera trap monitoring, 2018
Monitoring of wildlife at Hubbard Brook is essential to understand how these species are responding to forest and environmental condition over time, while also placing those wildlife species in the context of ecosystem structural and functional attributes. The presence and persistence of wildlife species common to an area can indicate suitable habitat conditions as well as refugia for less common species. Changes in species presence and activity, such as fewer to no sightings, may point to shifting conditions not suitable to the species missing from the area. Camera trap monitoring allows for continuous, non-obtrusive observation of many different species of wildlife and can be used as part of our understanding of current suitability of habitat condition. To better understand integrated forest condition, we established a camera trap network located at the Hubbard Brook Experimental Forest in the White Mountains of central New Hampshire. The cameras have logged over 1,500 wildlife observations, confirming the presence of many species, including those not previously reported (pine marten and river otter). A total of 15 mammal species have been detected and have also been effective at detecting some bird species, including the Northern Harrier. Natural history observations have provided insight into the lives of the species detected, including reproduction (Bull moose following cow during rut, moose calves, deer fawns), predation (red fox with snow-shoe hare) and presence of parasites (winter ticks on moose with hairless shoulders). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
CSM06 Seasonal summary of numbers of small mammals on miscellaneous traplines in prairie habitats that were trapped from 1 to 11 years at Konza Prairie
Data set contains seasonal summaries (spring, summer and autumn) of the number of individuals of each species of small mammal captured (relative abundance) on each prairie trapline. Each record contains year, season, trapline and number of individuals captured of each species. These live trap records are based on daily captures during 4-day trapping periods in spring (early March to early April), summer (late June to late July) and autumn (early October to mid-November) for each permanent trapline (two traplines per treatment). These treatments include annual burns, 2-year burns, 4-year burns and 10-year burns; none were grazed by bison. This data set includes 14 traplines sampled in autumn and spring and 30 traplines in summer.
CSM05 Seasonal summary of numbers of small mammals on the six LTER traplines in prairie habitats on which fire regime has been reversed at Konza Prairie
Data set contains seasonal summaries (spring and autumn) of the number of individuals of each species of small mammal captured (relative abundance) on each grassland trapline. Each record contains year, season, trapline and number of individuals captured of each species. These live trap records are based on daily captures during a single 4-day trapping period in spring (mid-March to early April) and autumn (late October to early December) for each of six permanent traplines established on two fire treatments (three traplines per treatment). These two fire treatments include one treatment that was changed from a 20-year burn to an annual burn and one that was changed from an annual burn to 20 years between fires. Bison do not graze these two habitat types.
CSM03 Seasonal summary of numbers of small mammals on the two LTER traplines in planted grassland (Brome fields) habitats at Konza Prairie
Data set contains seasonal summaries (spring, summer and autumn) of the number of individuals of each species of small mammal captured (relative abundance) on each woodland trapline. Each record contains year, season, trapline and number of individuals captured of each species. These live trap records are based on daily captures during a single 4-day trapping period in spring (early March to early April), summer (early July to late July) and autumn (mid-October to early December) for each of four permanent traplines established in two habitats (two traplines in gallery forest and two on limestone ledges). Bison did not graze any of the treatment units during the period of study.
Phrynus habitat selection
This data set comprises a single data file, which contains data on the abundance and distribution of the whipspider Phrynus longipes on the Luquillo Forest Dynamics Plot in July 2001. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Plant Apparency data - Habitat selection/Caracolus caracolla and other snails (2019 Ongoing)
Various habitat characteristics are presented, as well as the apparency of common plant taxa at 7 heights (every 0.5 m from ground level to 3 m). The files are divided because variables measured varied by year. This is Version 2020 of these data. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Habitat selection/Caracolus caracolla and other snails
Various habitat characteristics are presented, as well as the apparency of common plant taxa at 7 heights (every 0.5 m from ground level to 3 m). The files are divided because variables measured varied by year. This data set consists of 7 files. Data sets from 1991 through 1995 each have slightly different categories for measures of litter depth, canopy openness, or physical features (e.g. slope, aspect, soil type) that are not always included in later data sets. In addition, plant species recorded changed slightly over this time period, but were standardized by 1996. Various habitat characteristics are presented, as well as the apparency of common plant taxa at 7 heights (every 0.5 m from ground level to 3 m). Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Bisley Grid Habitat data 1994, 1999
The data set consists of one file containing data from the summers of 1994 and 1999. Various habitat characteristics are presented, as well as the apparency of common plant taxa at 7 heights (every 0.5 m from ground level to 3 m). However, the data for some plant species were not divided by height in 1994; only total apparency of those species is available for that year. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
MCR LTER: Coral Reef Resilience: North Shore Herbivorous Fish Counts, Habitat Associations, and Substrate, 2010
These data describe the species abundance, size distributions, and habitat associations of roving herbivorous fishes (fishes belonging to the families, Acanthuridae, Scaridae, and Siganidae) found in different habitats in the lagoon and forereef on the north shore of Moorea. Adult fishes and large juveniles were counted (and their size estimated) by a SCUBA diver or snorkeler on thirty-four 50 m by 5 m wide transects. After counting large fishes, the entire transect was swam a second time, with the diver looking exclusively for small juvenile fishes on a 1 m swath. In addition to recording the species identity and estimated size of each juvenile encountered, the diver also recorded the particular microhabitat each individual or group of individuals was associated with. Finally, to quantify the relative availability of different types of microhabitat, the diver conducted point contacts where the primary benthic substrate was identified at regular (1 m) intervals on the same transect. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Fecal glucocorticoid metabolite levels of American pika (Ochotona princeps) and habitat characteristics of their associated territories found in rock glaciers adjacent to Niwot Ridge and within Rocky Mountain National Park, 2018 - 2019.
To understand whether stress-associated hormones vary with metrics of habitat quality, we measured fecal glucocorticoid metabolite (FGM) levels in the American pika (Ochotona princeps), a small mammal with well-defined habitat (talus), that can vary in quality depending on the presence of rock ice features (RIFs). In 2018, we sampled pika scat from two types of RIFs: “active” rock glaciers thought to harbor subsurface ice recently, and “fossil” rock glaciers considered long devoid of subsurface ice (as classified by Janke 2005, 2007). Specifically, fecal pellets were collected from pika territories located in rock glaciers within eight sites along the Front Range of Colorado: four in Rocky Mountain National Park (2 active, 2 fossil) and four adjacent to Niwot Ridge (2 active, 2 fossil) (pika_fecal_glu_rg.aw.csv). To account for possible seasonal variation in pika FGM, scat samples were collected in the alpine spring and fall. To understand other influences of habitat quality on FGMs, we also measured fine-scale habitat differences between rock glaciers in 2019, including talus depth, clast size, and land cover metrics related to forage (pika_fecal_habitat_rg.aw.csv).
Comparative Bird Community Assessments in Grassland, Shrubland, and Woodland Habitats at the Sevilleta National Wildlife Refuge, New Mexico (1991-1997 and 2022-2023)
Across North America, avifauna abundance has declined by 30% since 1970 (Rosenberg, K.V. et al. 2019). Direct mortality from anthropogenic sources (pets, cars, collisions with building, power lines, wind turbines, etc.) and indirect mortality (habitat loss, disturbance, climate change, etc.) have both been major contributors to these declines (Loss, S.R. et al. 2015 and Calvert, A.M. et al. 2013). Variables such as migration patterns, family, breeding and non-breeding biomes show differing rates of decline (Rosenberg, K.V. et al. 2019). In New Mexico, there are three breeding biomes all classified with declining avian abundance. Avian abundance in grasslands has declined by 53.3% since 1970, western forests by 29.5% and arid lands by 17.0% (Rosenberg, K.V. et al. 2019). All three of these biomes also occur at the Sevilleta National Wildlife Refuge thus temporal declines in species richness and abundance are expected. This project was originally designed to sample the species richness and abundance of birds on the Sevilleta National Wildlife Refuge in three types of habitat: grassland, creosote shrubland and pinyon-juniper woodland. Surveys were conducted between January 1991 and May 1997 (Parmenter, R. 2016). Surveys were re-established in 2022 to document current species richness and abundance and to capture any temporal changes from the 90s data. Avian point count survey stations in grassland, creosote and pinyon-juniper habitats run through existing study sites which have all been subjected to intense research activity. Literature Cited A. M. Calvert, C. A. Bishop, R. D. Elliot, E. A. Krebs, T. M. Kydd, C. S. Machtans, G. J. Robertson, A synthesis of human-related avian mortality in Canada. Avian Conserv. Ecol. 8, art11 (2013). https://www.ace-eco.org/vol8/iss2/art11/ Loss, S. R., Will, T., Marra, P. P. 2015. Direct Mortality of Birds from Anthropogenic causes. Annu. Rev. Ecol. Evol. Syst. 46, 99–120. https://www.annualreviews.org/doi/10.1146/annurev-ecolsys-1124
figure data for "Subsurface radiation environment of Mars and its implication for shielding protection of future habitats" by L.Röstel, J.Guo et al. 2020
<pre>This data of dose rates at different elevations above and below the Martian surface was modeled using the GEANT4-based AtRIS toolkit. Please refer to the following paper for reference and a detailed description of the model and scaling: „Subsurface radiation environment of Mars and its implication for shielding protection of future habitats“, L.Röstel, J.Guo et al. 2020 JGR: planets. List of files: AbsorbedDosePrimariesAR.txt - figures 2 in the paper EquivalentDosePrimariesAR.txt - figure 3 AbsorbedDoseSiliconSlabScenarios.txt - figure 4 AbsorbedDoseWaterSphereScenarios.txt - figure 5 EquivalentDoseWaterSphereScenarios.txt - figure 6 NeutronFlux.txt - figure 7 RequiredShieldingDepth.txt - figure 8</pre>
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>
Fig. 3 Habitat of Philorhizus marggii Wrase DW, Assmann T (2008) A new species of Philorhizus Hope, 1838 from Greece (Coleoptera, Carabidae, Lebiini). ZooKeys 3: 1-10. doi: 10.3897/zookeys.3.19
<p>Habitat of Philorhizus marggii<br> Greece: Peloponnese/Taygetos: Profitis Illias, subalpine altitude, 16.V.2007, 2000-2400m a.s.l.</p> <p> </p>
Tree and habitat structure data from rainforest fragments and coffee plantations in the Anamalai Hills, Western Ghats, India
<p><strong>TITLE</strong></p><p><strong>Tree and habitat structure data from rainforest fragments and coffee plantations in the Anamalai Hills, Western Ghats, India</strong><br> </p><p><strong>DESCRIPTION</strong></p><p>This dataset contains point-centred quarter (PCQ) data on trees and habitat structure measurements data from rainforest fragments and some coffee plantations in the Valparai Plateau and Anamalai Tiger Reserve, Tamil Nadu, India. The data were gathered to quantity habitat parameters for bird and small carnivorous mamm community studies. Data were gathered mainly by T. R. Shankar Raman and Divya Mudappa (2000 to 2003), Hari Sridhar (2005), and Akshay Surendra (2019).</p><p><strong>Publications</strong></p><p>Specific portions of the dataset have been used in the following publications:</p><ul><li>Mudappa, D. 2001. <a href="https://hdl.handle.net/10603/101890">Ecology of the brown palm civet <i>Paradoxurus jerdoni</i> in the tropical rainforests of the Western Ghats, India</a>. Ph. D. thesis, Bharathiar University, Coimbatore. https://hdl.handle.net/10603/101890</li><li>Raman, T. R. S. 2001. <a href="https://archive.org/details/raman-2001-ph-d-thesis-iisc">Community ecology and conservation of mid-elevation tropical rainforest bird communities in the southern Western Ghats, India</a>. PhD thesis, Indian Institute of Science, Bangalore. https://archive.org/details/raman-2001-ph-d-thesis-iisc</li><li>Raman, T.R.S. 2006. <a href="https://doi.org/10.1007/s10531-005-2352-5">Effects of Habitat Structure and Adjacent Habitats on Birds in Tropical Rainforest Fragments and Shaded Plantations in the Western Ghats, India</a>. <i>Biodiversity and Conservation</i> 15: 1577–1607. https://doi.org/10.1007/s10531-005-2352-5</li><li>Sridhar, H., & Sankar, K. 2008. <a href="https://doi.org/10.1017/S0266467408004823">Effects of habitat degradation on mixed-species bird flocks in Indian rain forests</a>. <i>Journal of Tropical Ecology</i> 24: 135-147. https://doi.org/10.1017/S0266467408004823</li><li>Surendra, A. & Raman, T. R. S. 2022. <a href="https://doi.org/10.1101/2022.10.22.513365">Forest bird decline and community change over 19 years in long-isolated South Asian tropical rainforest fragments</a>. Preprint. <i>BioRxiv</i> 2022.10.22.513365. https://doi.org/10.1101/2022.10.22.513365<br> </li></ul><p>A related dataset is the following:<br>Raman, T. R. S. (2020). Data from: Effects of Habitat Structure and Adjacent Habitats on Birds in Tropical Rainforest Fragments and Shaded Plantations in the Western Ghats, India. <i>Dryad Dataset.</i> https://doi.org/10.5061/dryad.4mw6m907q<br> </p><p><strong>Curation and corrections</strong></p><p>Data were collated, curated, and corrected before this upload. Besides addition of new columns, explanations of metadata, and other corrections included few related to canopy measurements, effective girth of multi-stem trees, and species identification.</p><p><strong>Acknowledgements</strong></p><p>We are grateful to P. Jeganathan and P. R. Shankar for assistance with data collection in 2000. Others who assisted with field research, and funding agencies related to the specific studies, are acknowledged in the above publications. The data compilation and publication was carried out as part of a grant from Fondation Franklinia to NCF.</p><p><br><strong>CONTACTS</strong><br> </p><p>CONTACT #1<br>1. Name: T. R. Shankar Raman<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: trsr@ncf-india.org<br>5. ORCID: https://orcid.org/0000-0002-1347-3953</p><p>CONTACT #2<br>1. Name: Divya Mudappa<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: divya@ncf-india.org<br>5. ORCID: https://orcid.org/0000-0001-9708-4826</p><p>CONTACT #3<br>1. Name: Hari Sridhar<br>2. Work Address: Wildlife Institute of India, Post Bag #18, Chandrabani, Dehradun – 248001, Uttarakhand, India; Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: harisridhar1982@gmail.com<br>5. ORCID: https://orcid.org/0000-0003-3286-0120</p><p>CONTACT #4<br>1. Name: Akshay Surendra<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India; School of the Environment, Yale University, New Haven, CT – 06511, USA; New York Botanical Garden, 2900 Southern Blvd, Bronx, NY 10458<br>3. Work Phone: +91 821 2515601<br>4. Email address: akshaysurendra1@gmail.com<br>5. ORCID: https://orcid.org/0000-0003-2719-7432<br> </p><p><br><strong>GEOGRAPHIC COVERAGE</strong></p><p>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India</p><p>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p><p><br><strong>TEMPORAL COVERAGE</strong></p><p>1. Begins: 2000-01-01 (Year, Month, Day)<br>2. Ends: 2019-12-31 (Year, Month, Day)</p><p><br><strong>METHODS</strong></p><p>Methods involved are described in the publications listed above. The vegetation sampling methods are briefly described below.</p><p>PCQ data: Trees ≥30cm girth at breast height (gbh, at 1.3 m) were sampled in replicate point-centred quarter (PCQ) points in each of the sites (fragments or coffee plantations).</p><p>All trees in the PCQ plots were identified to species, or in a few cases to genus, using available field guides. Using a tape measure, distance from plot centre to the middle of the bole and GBH were recorded for each tree. At each of the PCQ plots, circular plots were laid to enumerate shrubs and cut trees and record presence or absence of lianas, cane, Lantana etc as described in the metadata. Canopy and leaf litter variables were measured at replicate points, spaced 25 to 50 m apart, in each site. Elevation readings were also taken at these points using an altimeter or handheld GPS. Canopy height was measured using a rangefinder. Percentage canopy cover was measured using a spherical densiometer at each of the 25 points in each site. Vertical stratification was assessed by noting presence or absence of foliage in the following height intervals (in metres): 0–1, 1–2, 2–4, 4–8, 8–16, 16–24, 24–32, and > 32, directly above and in a 0.5 m radius around each point. Leaf litter depth on the forest floor was measured using a calibrated wooden probe at each point. Where ground vegetation and litter were disturbed along trails, the samples were taken away from trails in the forest floor.</p><p><br><strong>FILES INCLUDED</strong><br>Besides the 00_README.txt file that contains this metadata, the dataset includes the following 7 files, whose details and contents are explained below. (Wherever used in the various files, NA implies not available.)<br> </p><p><strong>01) sites.csv -- Details of study sites</strong><br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>decimalLongitude: Longitude in decimal degrees North (WGS 84 datum)<br>decimalLatitude: Latitude in decimal degrees East (WGS 84 datum)<br>habitat: Habitat type as mature tropical rainforest, tropical rainforest fragment, or coffee plantation<br>Description: Description of the place<br> </p><p><strong>02) allpcqdata.csv -- Tree data from point-centred quarter (PCQ) surveys</strong><br>Year: Year of survey for bird and vegetation study<br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>Point_name: Name ID of point-centred quarter (PCQ) point as used within a survey year<br>pointID: Unique ID of point-centred quarter (PCQ) point including year of survey<br>Tree_no: Tree number ID given to the four trees in each PCQ plot (T1 to T4)<br>verbatimIdentification: Scientific name of tree species as originally written or identified<br>scientificName: Scientific name as currently identified under updated taxonomy<br>nativeAlien: Category indicating whether species is native or alien to the region/country<br>kingdom: Taxonomic Kingdom<br>phylum: Taxonomic Phylum<br>Distance_eff: Distance in metres from centre of PCQ plot to centre of tree trunk<br>Girth_eff: Girth in centimetres (cm) at breast height (1.3 m) of the tree after correction (using appropriate formula) in the case of multi-stemmed individuals<br>locationRemarks: Code for site name as originally used<br>SpCode: Species code as originally used during data entry<br>TreeHeight: Tree height in metres (only available in 2019 survey)<br>identificationRemarks: Notes related to identification if available<br>occurrenceRemarks: Notes related to multi-stemmed individuals (girths in cm) if available and note on one possibly errorneous girth<br> </p><p><strong>03) pcqlocations.csv -- Locations of sample PCQ points</strong><br>pointID: Unique ID of point-centred quarter (PCQ) point including year of survey<br>note: Site name code<br>decimalLatitude: Latitude in decimal degrees East (WGS 84 datum)<br>decimalLongitude: Longitude in decimal degrees North (WGS 84 datum)<br>coordinateUncertaintyInMeters: Approximate uncertainty of the location in metres<br> </p><p><strong>04) allhabitat.csv -- Data on habitat structure variables</strong><br>Year: Year of survey for bird and vegetation study<br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>Point: ID of replicate survey point within the Fragment<br>0-1m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 0-1 m above ground<br>1-2m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 1-2 m above ground<br>2-4m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 2-4 m above ground<br>4-8m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 4-8 m above ground<br>8-16m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 8-16 m above ground<br>16-24m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 16-24 m above ground<br>24-32m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 24-32 m above ground<br>over32m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band greater than 32 m above ground<br>VertStrata: Number of vertical strata with foliage (sum of preceding 8 columns)<br>CanopyHeight: Canopy height in metres<br>CanopyOpenness: Canopy openness in percentage as measured using a spherical densiometer<br>CanopyCover: Canopy cover (closure) in percentage as measured using a spherical densiometer<br>CanopyOverlap: Canopy overlap rank: 0-open sky above; 1-branches above barely touching; 2-overlapping branches above, sky visible; 3-overlapping branches, sky not visible<br>UC: Canopy overlap rank as above, for understorey vegetation only<br>MC: Canopy overlap rank as above, for the midstorey only<br>CC: Canopy overlap rank as above, for the upper canopy only<br>Altitude: Altitude above sea leavel in metres, measued from hand-held altimeter or GPS device<br>RfShrub: Number of shrubs (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>Coffee: Number of coffee bushes (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>Maesopsis: Number of alien Maesopsis eminii stems (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>Strobilanthes: Number of Strobilanthes shrubs (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>TotalShrub: Total number of shrubs within 2 m radius of point<br>Liana: Presence (1) or absence (0) of woody lianas within 5 m radius of point<br>Cane: Presence (1) or absence (0) of cane (Calamus sp.) within 2 m radius of point<br>Lantana: Presence (1) or absence (0) of Lantana camara shrubs within 2 m radius of point<br>Bamboo: Presence (1) or absence (0) of bamboo culms within 2 m radius of point<br>LeafLitter: Depth of leaf litter in cm (to 0.5 cm accuracy) measured using a calibrated wooden probe<br>CutTrees: Number of cut trees within 5 m radius of point<br> </p><p><strong>05) gbifnames.csv -- Results of GBIF name matching tool</strong><br>sno: Serial number<br>verbatimScientificName: Scientific name of tree species as originally written or identified<br>scientificName: Scientific name after matching with Global Biodiversity Information Facility (GBIF) database to lowest taxonomic level<br>sciNameWithAuthor: Scientific name with author as provided by GBIF name matching tool<br>key: GBIF key as provided by GBIF name matching tool<br>matchType: Type of match as provided by GBIF name matching tool<br>confidence: Confidence as provided by GBIF name matching tool<br>status: Status as accepted name or synonym as provided by GBIF name matching tool<br>rank: Taxonomic rank as provided by GBIF name matching tool<br>kingdom: Kingdom as provided by GBIF name matching tool<br>phylum: Phylum as provided by GBIF name matching tool<br>class: Class as provided by GBIF name matching tool<br>order: Order as provided by GBIF name matching tool<br>family: Family as provided by GBIF name matching tool<br>genus: Genus as provided by GBIF name matching tool<br>species: Species as provided by GBIF name matching tool<br>canonicalName: Canonical name as provided by GBIF name matching tool<br>authorship: Author of name as provided by GBIF name matching tool<br> </p><p><strong>06) plots2000.csv -- Data from 5 m radius circular plots in select sites</strong><br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>PlotID: ID of 5 m radius plot<br>Treeno: Serial number of tree in the plot<br>verbatimIdentification: Scientific name of tree species as originally written or identified<br>scientificName: Scientific name as currently identified under updated taxonomy<br>Girth_eff: Girth in centimetres (cm) at breast height (1.3 m) of the tree after correction (using appropriate formula) in the case of multi-stemmed individuals<br>nativeAlien: Category indicating whether species is native or alien to the region/country<br>kingdom: Kingdom as provided by GBIF name matching tool<br>phylum: Phylum as provided by GBIF name matching tool<br>occurrenceRemarks: Notes related to multi-stemmed individuals (girths in cm) if available and identification</p><p> </p><p><strong>07) anampcqs4gbif.rmd -- Text file with code in the R statistical and programming language</strong> </p><p>This R code was used for converting data in this Zenodo dataset into Darwin Core occurrence dataset for upload to the Global Biodiversity Information Facility (GBIF, https://www.gbif.org). The published dataset can now be accessed at: https://doi.org/10.15468/cmsveh</p><p> </p><p><strong>Changes in Version 2</strong></p><p>In sites.csv, changed habitat from "Rainforest" to "Tropical rainforest fragment" for Puthuthottam</p><p>Added the anampcqs4gbif.rmd file with R code</p>
Phlorest phylogeny derived from Grollemund et al. 2015 'Bantu expansion shows habitat alters the route and pace of human dispersals'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Grollemund R, Branford S, Bostoen K, Meade A, Venditti C & Pagel M. 2015. Bantu expansion shows habitat alters the route and pace of human dispersals. Proceedings of the National Academy of Sciences of the USA, 112(43), 13296-13301.</p> </blockquote>
Data: "Using butterfly survey data to model habitat associations in urban developments", JEJ Cooper et al., (2023)
<p>This data package has been used to examine the responses of UK butterfly species </p> <p>to different features of the urban environment. 'JC_WCBSmodel.Rdata' presents the</p> <p>butterfly abundance data, and supporting information about </p> <p>species and sites. This data can be fed through the script '04_model_builder.R', to </p> <p>produce the models reported in the research article. '00_functions.R' is a script </p> <p>containing functions which support the modelling process, which is loaded as part of </p> <p>the 04_model_builder script. </p> <p> </p> <p>Summaries of the resulting models are an output of that script - </p> <p>'Butterfly_GAM_Outputs.xlsx'. These are represented graphically in the manuscript, </p> <p>using scripts '06_01_Map'.R:'06_03_Cross_Validation'. '06_04_Model_Metric.R' </p> <p>is a further summary of the .xlsx file, found in the Supplementary Materials. </p> <p>'06_05_graphic_4_twitter.R' produces a condensed version of the figure resulting </p> <p>from the script '06_02_Metric_Summary.R'</p> <p> </p> <p>Dataset descriptions are found in the attached readme.txt</p> <p>........................................................................................</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
Redistribution of the Natura 2000 habitat map of Flanders, partim habitat type 3260 (version 2023)
<p>This is a redistribution of a subdataset of the data source '<a href="https://www.vlaanderen.be/datavindplaats/catalogus/biologische-waarderingskaart-en-natura-2000-habitatkaart-toestand-2023">Biologische Waarderingskaart en Natura 2000 Habitatkaart - Toestand 2023</a>', originally published by the Research Institute for Nature and Forest (INBO) and distributed by 'Digitaal Vlaanderen' under a CC-BY compatible license. It is redistributed for reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes.</p><p>The subdataset is a shapefile of line segments of the Natura 2000 habitat type 3260 (Watercourses of plain to montane levels with the <i>Ranunculion fluitantis</i> and <i>Callitricho-Batrachion</i> vegetation) that correspond with its presence in watercourses in the Flemish Region, identical to the shapefile Hab3260 in the original data source.</p><p>The data source is produced, owned and administered by the Research Institute for Nature and Forest (INBO, Department of Environment of the Flemish government).</p>
Data and Code for "Why are generalists the 'winners' of habitat loss? Unveiling the process underlying specialist-generalist replacements in fragmented landscapes"
<p><span>Data and R-based workflow for the study "Why are generalists the ‘winners’ of habitat loss? Unveiling the process underlying specialist-generalist replacements in fragmented landscapes".</span></p>
Map of standing water habitat types and regionally important biotopes in Flanders
<p>This map is a combination of the <a href="https://zenodo.org/records/13865531">standardized habitat map of Flanders</a> (version habitatmap_stdized_2023_v1) and <a href="https://zenodo.org/records/14203168">the watersurface map of Flanders</a> (version watersurfaces_2024). It contains standing water Natura 2000 habitat types (2190_a and 31xx) and regionally important biotopes (rbbah) in Flanders.</p> <p>The polygons with 2190_a habitat (dune slack ponds) are generated by selecting all watersurface polygons that overlap with dune habitat polygons (21xx) of the standardized habitat map.</p> <p>For each of the other aquatic habitat types (31xx and rbbah) we select the watersurface polygons that overlap with the selected habitat type polygons of the standardized habitat map. We also select polygons of the standardized habitat map containing standing water types but that do not overlap with polygons of the watersurface map.</p> <p>The <code>watersurfaces_hab.gpkg</code> file is a GeoPackage that contains:</p> <ul> <li><code>watersurfaces_hab_polygons</code>: a spatial layer with the selected polygons that contain standing water habitat types or regionally important biotopes. </li> <li><code>watersurfaces_hab_types</code>: a table with information on standing water habitat types and regionally important biotopes in each watersurface polygon.</li> </ul> <p>The R-code for creating the <code>watersurfaces_hab</code> data source can be found in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/58138a8/src/generate_watersurfaces_hab">'n2khab-preprocessing' at commit 58138a8</a>.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://github.com/inbo/n2khab">n2khab</a>.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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
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