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104 results for “habitat characteristics”
Influence of European beech (Fagus sylvatica) rot hole habitat characteristics on invertebrate community structure and diversity - Dataset
<p>The data and R scripts pertinent to the Journal of Insect Science manuscript titled "Influence of European beech (<em>Fagus sylvatica</em>) rot hole habitat characteristics on invertebrate community structure and diversity". Environmental variables are given in "Rot Hole and Site Data", whilst community data are given as counts in "Rot Hole Community Data - counts" and as relative abundance in "Rot Hole Community Data - relative abundances".</p>
Physiological stress of American pika (Ochotona princeps) and associated habitat characteristics for Niwot Ridge, 2018 - 2019
Temporal variation in a metric of stress might signify changes in an organism’s internal or external environment, but spatial variation in stress response could also signify spatial variation in habitat quality. Any stress response to habitat quality can be complicated or modulated, however, by strategies like escape in space (e.g., animal movements) or time (e.g., seed banks) that allow access to different conditions. Spatial and temporal variation in response to potential stressors has received little study in wild animals, especially at scales appropriate for relating stress to specific habitat characteristics. Here, we use the American pika (Ochotona princeps), a highly territorial small mammal, to investigate stress response within and among territories. For individually territorial animals such as pika, differences in habitat quality should lead to differences in the levels of stress exhibited by territory owners. In this study, we indexed stress by measuring stress hormone metabolites in feces collected non-invasively from pika territories. Feces were collected from 20 territories every two weeks from June to September of 2018 (pika_fecal_glut.aw.data.csv). In 2019, habitat metrics were recorded at each territory (pika_fecal_glut_habitat.aw.data.csv).
Dataset accompanying Riesch et al. 2020. Grazing by wild red deer maintains characteristic vegetation of semi-natural open habitats: Evidence from a 3-year exclusion experiment. Applied Vegetation Science
<p>This repository contains vegetation community data used by Riesch et al. 2020 in an article accepted in Applied Vegetation Science.</p> <p>Metadata are provided in the first excel worksheet. For further details please see the original article.</p>
Species richness, composition and microhabitat characteristics of non-volant terrestrial mammals in disturbed habitats
<b>Description: </b><p>A study on the small mammals communities was carried out in disturbed habitats aroundsabah, namely university malaysia sabah (ums), klias peat swamp forest reserve(klias), kawang forest reserve (kawang), kalabakan forest reserve (safe) and maliaubasin conservation area (maliau). the objectives were (1) to determine the speciesrichness and composition of non-volant small mammal communities in disturbedhabitats; (2) to characterize the microhabitat-use patterns of the non-volant smallmammal communities in disturbed habitats; and (3) to determine the microhabitatpreferences of the non-volant small mammal communities in disturbed habitats. the aimof this study was to investigate how the habitat disturbance affects the species richness,community compositions and microhabitat-use pattern of the small mammals. this studywas conducted from october 2014 to march 2015 with a total sampling effort of 540trap-nights. overall, 71 individuals representing 14 species were successfully caughtduring this study. the species richness peaked at safe, and then declined at the rest ofthe study sites. habitat variables analysis showed that all study sites were divided intothree distinctive groups in terms of habitat types. canonical discriminant functionanalysis were used to analyze the microhabitat preferences and use-pattern of smallmammals and results showed the preferences of small mammals towards shrub cover(rattus rattus and callosciurus notatus), litter cover (callosciurus prevostii and echinorexgymnurus) and herbs limber (tupaia gracilis). The locations of the traps have not been given longitute and latitute as there were set on animal trails approximately 20metres from one another. </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/150"><b>Species richness, composition and microhabitat characteristics of non-volant terrestrial mammals in disturbed habitats</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Universiti Malaysia Sabah (Grant)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiverstiy Council (Research licence NA)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3265704">here</a></p><p><b>Files: </b>This consists of 1 file: Veg_Volent_mammals.xlsx</p><p><b>Veg_Volent_mammals.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Vegetation cover</b> (described in worksheet Vegetation_cover)</p><p>Description: The estimated canopy cover and percentage of ground cover of traps across disturbed habitats</p><p>Number of fields: 19</p><p>Number of data rows: 180</p><p>Fields: </p><ul><li><b>Date</b>: Date the vegetation cover was collected (Field type: Date)</li><li><b>Location</b>: Where data was collected (Field type: Location)</li><li><b>Habitat_type</b>: Habitat type (Field type: Categorical)</li><li><b>Trap_station</b>: Transect and trap number (Field type: ID)</li><li><b>Canopy_cover</b>: Percentage of canopy cover (Field type: Numeric)</li><li><b>Canopy_height</b>: Canopy height (Field type: Numeric)</li><li><b>Herbs_Climber</b>: Any climbers seen on trees for example vines and lianas (Field type: Numeric)</li><li><b>Tree_GBH_>10CM</b>: Tree girth at breast height of 10 cm (Field type: Numeric)</li><li><b>Tree_GBH_>30CM</b>: Tree girth at breast height of 30 cm (Field type: Numeric)</li><li><b>Tree_GBH_>60CM</b>: Tree girth at breast height of 60 cm (Field type: Numeric)</li><li><b>Tree_GBH_>90CM</b>: Tree girth at breast height of 90 cm (Field type: Numeric)</li><li><b>Fallen_trees_bran</b>: Percentage cover (Field type: Numeric)</li><li><b>Bareground</b>: Percentage cover (Field type: Numeric)</li><li><b>Shrub</b>: Percentage cover (Field type: Numeric)</li><li><b>grass</b>: Percentage cover (Field type: Numeric)</li><li><b>Rock</b>: Percentage cover (Field type: Numeric)</li><li><b>Litter</b>: Percentage cover (Field type: Numeric)</li><li><b>Water</b>: Percentage cover (Field type: Numeric)</li><li><b>Twig</b>: Percentage cover (Field type: Numeric)</li></ul></li><li><p><b>Non volent mammal abundance</b> (described in worksheet Non_volent_mammals)</p><p>Description: The abundance of non-volant terrestrails mammals caught across disturbed habitats. Growth and sex measurements takens</p><p>Number of fields: 17</p><p>Number of data rows: 490</p><p>Fields: </p><ul><li><b>Date</b>: Date the vegetation cover was collected (Field type: Date)</li><li><b>Location</b>: Where data was collected (Field type: Location)</li><li><b>Transect</b>: Transect number (Field type: ID)</li><li><b>Habitat</b>: Habitat type (Field type: Categorical)</li><li><b>Trap_station</b>: Transect and trap number (Field type: ID)</li><li><b>Species</b>: Species of non volent mammals caught in trap (Field type: Taxa)</li><li><b>Weight</b>: Weight of caught non volent mammal (Field type: Numeric)</li><li><b>Ear</b>: Ear measurment of caught non volent mammal (Field type: Numeric)</li><li><b>Hind_leg</b>: Hind leg measurement of caught non-volent mammal (Field type: Numeric)</li><li><b>Head_body</b>: Head to body measurement of caught non volent mammal (Field type: Numeric)</li><li><b>Tail</b>: Tail length of caught non volent mammal (Field type: Numeric)</li><li><b>Sex</b>: Sex of caught non volent mammal (Field type: Categorical)</li><li><b>Sexual_activity</b>: Sexual maturity of caught non volent mammal (Field type: Categorical)</li><li><b>Age</b>: Age class of caught non volent mammal (Field type: Categorical)</li><li><b>Trap_condition</b>: Trap condition (Field type: Categorical)</li><li><b>Trap_open_closed</b>: Trap open or closed (Field type: Categorical)</li><li><b>Bait</b>: Bait taken or intact (Field type: Categorical)</li></ul></li></ol><p><b>Date range: </b>2014-10-22 to 2015-03-30</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Chordata<br> -  - Mammalia<br> -  -  - Erinaceomorpha<br> -  -  -  - Erinaceidae<br> -  -  -  -  - <i>Echinosorex</i><br> -  -  -  -  -  - <i>Echinosorex gymnura</i><br> -  -  - Rodentia<br> -  -  -  - Muridae<br> -  -  -  -  - <i>Lenothrix</i><br> -  -  -  -  -  - <i>Lenothrix canus</i><br> -  -  -  -  - <i>Leopoldamys</i><br> -  -  -  -  -  - <i>Leopoldamys sabanus</i><br> -  -  -  -  - <i>Maxomys</i><br> -  -  -  -  -  - <i>Maxomys rajah</i><br> -  -  -  -  -  - <i>Maxomys surifer</i><br> -  -  -  -  - <i>Niviventer</i><br> -  -  -  -  -  - <i>Niviventer cremoriventer</i><br> -  -  -  -  - <i>Rattus</i><br> -  -  -  -  -  - <i>Rattus rattus</i><br> -  -  -  - Sciuridae<br> -  -  -  -  - <i>Callosciurus</i><br> -  -  -  -  -  - <i>Callosciurus adamsi</i><br> -  -  -  -  -  - <i>Callosciurus notatus</i><br> -  -  -  -  -  - <i>Callosciurus prevostii</i><br> -  -  -  -  - <i>Sundasciurus</i><br> -  -  -  -  -  - <i>Sundasciurus lowii</i><br> -  -  - Scandentia<br> -  -  -  - Tupaiidae<br> -  -  -  -  - <i>Tupaia</i><br> -  -  -  -  -  - <i>Tupaia dorsalis</i><br> -  -  -  -  -  - <i>Tupaia glis</i><br> -  -  -  -  -  - <i>Tupaia gracilis</i><br></div><p></p>
Figure 3 in Nest characteristics and breeding success of Sarus Crane, Antigone antigone (Linnaeus, 1758) (Aves: Gruidae) in different habitats at Dhanauri Wetland, Uttar Pradesh, India
Figure 3. Nest of Sarus Crane in perennial wetland.
Figure 4 in Nest characteristics and breeding success of Sarus Crane, Antigone antigone (Linnaeus, 1758) (Aves: Gruidae) in different habitats at Dhanauri Wetland, Uttar Pradesh, India
Figure 4. Female individual maintaining nest.
Figure 6 in Nest characteristics and breeding success of Sarus Crane, Antigone antigone (Linnaeus, 1758) (Aves: Gruidae) in different habitats at Dhanauri Wetland, Uttar Pradesh, India
Figure 6. Correlation between mean water depth and nest height.
Figure 1 in Nest characteristics and breeding success of Sarus Crane, Antigone antigone (Linnaeus, 1758) (Aves: Gruidae) in different habitats at Dhanauri Wetland, Uttar Pradesh, India
Figure 1. Camera trap (in red circle) used during the study.
Figure 1 in Roost characteristics and habitat preferences of Indian flying fox (Pteropus giganteus) in urban areas of Lahore, Pakistan
Figure 1. GIS-based map of Jinnah garden showing roosts of Indian flying fox populations.
Scripts from: Remotely sensed microhabitat characteristics associated with Haematopus palliatus (American Oystercatcher) nest-site selection can inform beach habitat restoration along the U.S. Atlantic Coast
Open the record for dataset details and reuse information.
Habitat characteristics and priority effects shape fish and invertebrate assemblages inhabiting the coral <i>Pocillopora grandis</i> in Hawai'i
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Using habitat, morphological, and genetic characteristics to delineate the subspecies of Sharp-Tailed Grouse in south-central Wyoming
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Data from: Bioacoustic assessment of mangrove health: Influence of habitat characteristics on bird richness
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Habitat features and colony characteristics influencing ant personality and its fitness consequences
<p>Several factors can influence individual and group behavioral variation that can have important fitness consequences. In this study, we tested how two habitat types (semi-natural meadows and meadows invaded by <i>Solidago</i> plants) and factors like colony and worker size and nest density influence behavioral (activity, meanderness, exploration, aggression, nest displacement) variation on different levels of the social organization of <i>Myrmica rubra </i>ants and how these might affect the colony productivity. We assumed that the factors within the two habitat types exert different selective pressure on individual and colony behavioral variation that affects colony productivity. Our results showed individual-/colony-specific expression of both mean and residual behavioral variation of the studied behavioral traits. Although habitat type did not have any direct effect, habitat-dependent factors, like colony size and nest density influenced the individual mean and residual variation of several traits. We also found personality at the individual level and at the colony level. Exploration positively influenced the total- and worker production in both habitats. Worker aggression influenced all the productivity parameters in semi-natural meadows, whereas activity had a positive effect on the worker and total production in invaded meadows. Our results suggest that habitat type, through its environmental characteristics, can affect different behavioral traits both at the individual and colony level and those with a high influence on colony productivity will form the personality of individuals. Our results highlight the need for complex studies on the behavioral variation of social insects to fully understand the effects shaping their behavior and productivity parameters.</p>
High spatial resolution mapping identifies habitat characteristics of the invasive vine Antigonon leptopus on St. Eustatius (Lesser Antilles)
<p>On the Caribbean island of St. Eustatius, Coralita (<i>Antigonon leptopus</i>)<i> </i>is an aggressive invasive vine posing major biodiversity conservation concerns. The generation of distribution maps can address these conservation concerns by helping to elucidate the drivers of invasion. We test the use of support vector machines to map the distribution of Coralita on St. Eustatius at high spatial resolution and use this map to identify potential landscape and geomorphological factors associated with Coralita presence. This latter step was performed by comparing the actual distribution of Coralita patches to a random distribution of patches. To train the support vector machine algorithm, we used three vegetation indices and seven texture metrics derived from a 2014 WorldView-2 image. The resulting map shows that Coralita covered 3.18% of the island in 2014, corresponding to an area of 64 ha. The mapped distribution was highly accurate, with 93.2% overall accuracy (Coralita class producer's accuracy: 76.4%, user's accuracy: 86.2%). Using this classification map, we found that Coralita is not randomly distributed across the landscape, occurring significantly closer to roads and drainage channels, in areas with higher accumulated moisture, and on flatter slopes. Coralita was found more often than expected in grasslands, disturbed forest and urban areas, but was relatively rare in natural forest. These results highlight the ability of high spatial resolution data from sensors such as WorldView-2 to produce accurate invasive species, providing valuable information for predicting current and future spread risks and for early detection and removal plans.</p>
Data from: Linking habitat composition, local population densities and traffic characteristics to spatial patterns of ungulate-train collisions
1. Total length of railways worldwide exceeds 1 million kilometres and recent railway development directly impacts wildlife because of animal-train collisions. Few studies, however, have analysed factors driving ungulate-train collisions. 2. We analysed over 3500 ungulate-train collisions including roe deer, red deer, wild boar, and moose collected in 2012-2015 in Poland. We compared train traffic characteristics (e.g. traffic intensity, speed, rail curvature), land-use and habitat characteristics (e.g. share of forests and build-up areas) and local ungulate population densities at collision sites and random sites distributed along the rail network. 3. Forest coverage generally increased, while urban areas decreased ungulate collision risk. Local density of ungulate species was strongly positively related to the relative collision risk in all four ungulate species, but above certain densities, the risk levelled off for all four species. 4. Train speed and train traffic intensity were positively associated with elevated collision risk in all four species, but the latter in a non-linear manner reached an asymptote at the level of ca. 10 trains per day. Rail curvature also increased probability of collisions with roe deer and red deer and possibly also wild boar. 5. Mortality rate of ungulates on railways in Poland is estimated to be 0.13-0.42% of annual hunting bags of studied species assuming that only one individual is killed at each occasion and ignoring undetected collisions. These values are expected to increase in near future due to increasing train speed in Central European countries. 6. Synthesis and applications. Ungulate-train collisions spots are characterised by surrounding forest, rail curvature, high train speed, and a moderate to high train traffic intensity. To reduce collision risk in a cost-effective way, we suggest to prioritise mitigation actions at sections of the railway characterized by those factors, e.g. by fencing and various warning devices. Due to nonlinear correlation between collision risk and population density, reducing density of ungulates will most likely reduce collision risk only marginally, and only in regions of low population densities where collision risk is relatively low anyway.
Supplementary material 1 from: Sosa-López JR, Díaz Bernal NN, Padilla E, Briones-Salas M (2023) Analysis of the effects of habitat characteristics, human disturbance and prey on felids presence using long-term community monitoring information. Nature Conservation 53: 279-295. https://doi.org/10.3897/natureconservation.53.104135
Sampling sites and dates on which the camera-traps were installed and Generalized linear mixed models (GLMM) for Puma, Bobcat and Margay
Data from: The effects of phylogeny, habitat, and host characteristics on the thermal sensitivity of helminth development
<p>Helminth parasites are part of almost every ecosystem, with more than 300 000 species worldwide. Helminth infection dynamics are expected to be altered by climate change, but predicting future changes is difficult due to lacking thermal sensitivity data for greater than 99.9% of helminth species. Here, we compiled the largest dataset to date on helminth temperature sensitivities and used the Metabolic Theory of Ecology to estimate activation energies (AEs) for parasite developmental rates. The median AE for 129 thermal performance curves was 0.67, similar to non-parasitic animals. Although exceptions existed, related species tended to have similar thermal sensitivities, suggesting some helminth taxa are inherently more affected by rising temperatures than others. Developmental rates were more temperature-sensitive for species from colder habitats than those from warmer habitats, and more temperature sensitive for species in terrestrial than aquatic habitats. AEs did not depend on whether helminth life stages were free-living or within hosts, whether the species infected plants or animals, or whether the species had an endotherm host in its life cycle. The phylogenetic conservatism of AE may facilitate predicting how temperature change affects the development of helminth species for which empirical data are lacking or difficult to obtain.Helminth parasites are part of almost every ecosystem, with more than 300 000 species worldwide. Helminth infection dynamics are expected to be altered by climate change, but predicting future changes is difficult due to lacking thermal sensitivity data for greater than 99.9% of helminth species. Here, we compiled the largest dataset to date on helminth temperature sensitivities and used the Metabolic Theory of Ecology to estimate activation energies (AEs) for parasite developmental rates. The median AE for 129 thermal performance curves was 0.67, similar to non-parasitic animals. Although exceptions existed, related species tended to have similar thermal sensitivities, suggesting some helminth taxa are inherently more affected by rising temperatures than others. Developmental rates were more temperature-sensitive for species from colder habitats than those from warmer habitats, and more temperature sensitive for species in terrestrial than aquatic habitats. AEs did not depend on whether helminth life stages were free-living or within hosts, whether the species infected plants or animals, or whether the species had an endotherm host in its life cycle. The phylogenetic conservatism of AE may facilitate predicting how temperature change affects the development of helminth species for which empirical data are lacking or difficult to obtain.</p>
The relative effects of pace of life and habitat characteristics on the evolution of sexual ornaments: a comparative assessment
<p><span>Selection may favor greater investment into sexual ornaments when opportunities for future reproduction are limited (e.g., due to high adult mortality). However, a key driver of mortality, predation, typically selects against elaborate sexual ornaments. Here, we examine the evolution of sexual ornaments in a group of killifishes, which have marked contrasts in life-history strategy between species and inhabit environments that differ in their accessibility to aquatic predators. We first assessed if the size of sexual ornaments (unpaired fins) influenced swimming performance and found that larger fins negatively affected swimming performance. Second, we investigated whether the evolution of larger ornamental fins is driven primarily by the pace of life-history (i.e., investment into current vs future reproduction) or habitat type (as a proxy for predation risk). We found that males from species inhabiting ephemeral habitats with lower predation risk had both larger fins and more pronounced sexual dimorphism in fin size, compared to males from more accessible permanent habitats. Our results indicate that enlarged ornamental fins impair locomotion and evolve more frequently in environments that are less accessible to predators, but with no clear association to the pace of life-history. We provide a rare link between the evolution of sexual ornaments, decreases in locomotion, and natural selection on ornaments through vulnerability to predation.</span></p>
FIG. 3 in Burrow Characteristics and Habitat Associations of Gopher Tortoises in Urban Pine Rockland Reserves (Miami, Florida, USA)
FIG. 3. Characteristics of Gopher Tortoise burrows at two sites in Miami-Dade County, Florida, USA. (A) Polar plots for Gopher Tortoise burrow orientations. There was no trend in direction of burrow opening at either site. (B) Burrow openings were wider at Zoo Miami than at the Deering Estate, and in both sites active burrows were wider than inactive burrows. (C) Burrows were far longer at the Deering Estate than at Zoo Miami, and active burrows were far longer than inactive burrows at both sites.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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.