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61 results for “habitat disturbance”
Dataset of habitat quality does not predict animal population abundance on frequently disturbed landscapes
<p>The data presented here are related to the research article entitled "Habitat quality does not predict animal population abundance on frequently disturbed landscapes". Using an individual-based model, we simulated movement of theoretical individuals in a dynamically disturbed landscape and quantified the error of predicting population spatial relative abundance using an habitat model. This dataset provides the Earth Mover's Distance (EMD) as prediction error measure obtained in simulations with varying individual step length and disturbance frequency.</p>
Figure 1 in Variables Affecting Habitat Use Of Hume'S Pheasant In Two Disturbed Sites In Northern Thailand
Figure 1. Map showing the locations of Doi Chiang Dao and Mae- Lao Mae-Sae Wildlife Sanctuaries and Doi Khun Mae Daet located in northern Thailand.
Habitat occupancy of the critically endangered Chinese pangolin (Manis pentadactyla) under human disturbance in an urban environment: Implications for conservation
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Dataset of habitat quality does not predict animal population abundance on frequently disturbed landscapes
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The influence of human disturbances on the spatio-temporal habitat selection patterns of roe deer near Trento (Italy)
<p>This is the dataset used for the MSc. thesis of Matthijs Hinkamp for the master Earth Sciences (Environmental Management track) at the University of Amsterdam.</p> <p>In this thesis the Individual Movement Sequence Analysis Method (IM-SAM) was applied to analyse the influence of human disturbances on the sequential habitat use of roe deer in northern Italy. While it is known that in this area the roe deer populations are affected by anthropogenic pressures, the actual spatial and temporal implications of such pressures are unknown.</p> <p>The input for IM-SAM consisted of habitat sequences, which were obtained through tracking data from the Fondazione Edmund Mach, land use and land cover maps and temporal information. Based on exploratory dissimilarity trees for the real behavioural sequences, several simulation profiles were established, each with a different habitat use pattern.</p> <p>The results show that most roe deer prefer isolated forest areas, but alternating patterns are definitely present. Several co variables were assessed: the influence of the hunting season, increased pressure during weekends, differences between protected and unprotected areas and changes in the average NDVI. It seems that roe deer are affected by the hunting pressure in September. This month shows an increase in the prevalence of alternating profiles, which indicates that roe deer tend to change their habitat use during periods of the day when hunting takes place. It cannot be concluded that habitat use patterns are altered during the weekends and the differences between sequences in protected and unprotected areas seem to be relatively small. However, some results indicate that alternating habitat use is a strategy for some roe deer in protected areas. When looking at the NDVI values, it becomes clear that the average NDVI is higher overall for the alternating profiles, but this just underlines the general conclusions drawn about the habitat use strategies in September.</p>
Data from: Habitat use and seed removal by invasive rats (Rattus rattus) in disturbed and undisturbed rainforest, Puerto Rico
Despite frequent occurrences of invasive rats (Rattus spp.) on islands, their known effects on forests are limited. Where invasive rats have been studied, they generally have significant negative impacts on native plants, birds, and other animals. This study aimed to determine invasive rat distribution and effects on native plant populations via short-term seed removal trials in tropical rain forest habitats in the Luquillo Experimental Forest, Puerto Rico. To address the first objective, we used tracking tunnels (inked and baited cards inside tunnels enabling animal visitors' foot prints to be identified) placed on the ground and in the lower canopy within disturbed (treefall gaps, hurricane plots, stream edges) and undisturbed (continuous forest) habitats. We found that rats are present in all habitats tested. Secondly, we compared seed removal of four native tree species (Guarea guidonia, Buchenavia capitata, Tetragastris balsamifera, and Prestoea acuminata) between vertebrate-excluded and free-access treatments in the same disturbed and undisturbed habitats. Trail cameras were used to identify animals responsible for seed contact and removal. Black rats (R. rattus) were responsible for 65.1% of the interactions with seeds, of which 28.6% were confirmed seed removals. Two plant species had significantly more seeds removed in disturbed (gaps) than undisturbed forest. Prestoea acuminata had the lowest seed removal (9% in 10 d), whereas all other species had >30% removal. Black rats are likely influencing fates of seeds on the forest floor, and possibly forest community composition, through dispersal or predation. Further understanding of rat-plant interactions may be useful for formulating conservation strategies.
Breaking ecological barriers: anthropogenic disturbance leads to habitat transitions, hybridization, and high genetic diversity
<p>Genetic diversity is expected to erode in disturbed habitats through strong selection, local extinctions, and recolonization associated with genetic bottlenecks and restricted gene flow. Despite this general prediction and over three decades of population genetics studies, our understanding of the long-term effect of environmental disturbance on local and regional genetic diversity remains limited. We conducted a population genetic survey of the microcrustacean <i>Daphnia</i> across a landscape subject to anthropogenic stressors from a century of industrial mining. At the local scale we found moderate genetic diversity (i.e., low clonal diversity), characteristic of habitat-specific selective sweeps and local extinctions, but high diversity and strong genetic structure at the regional scale despite the shared watershed of many lakes and exceptional dispersal ability of daphniids. Many habitats experienced changes in species assemblages, with the obligate asexual <i>Daphnia pulex</i> lineages—known only to inhabit ponds—dominating disrupted urban lakes. This habitat transition (pond to lake) was likely facilitated by the disruption of ecological barriers maintaining the genomic separation of these young species. Thus, disrupted habitats can exhibit complex and unexpected genetic patterns of local extinctions and recolonizations, followed by habitat transitions, hybridization and potential speciation events that are difficult to predict and should not be underestimated.</p>
Satellite-based habitat monitoring reveals long-term dynamics of deer habitat in response to forest disturbances
<p class="StandardohneEinzug">Disturbances play a key role in driving forest ecosystem dynamics, but how disturbances shape wildlife habitat across space and time often remains unclear. A major reason for this is a lack of information about changes in habitat suitability across large areas and longer time periods. Here, we use a novel approach based on Landsat satellite image time series to map seasonal habitat suitability annually from 1986 to 2017. Our approach involves characterizing forest disturbance dynamics using Landsat-based metrics, harmonizing these metrics through a temporal segmentation algorithm, and then using them together with GPS telemetry data in habitat models. We apply this framework to assess how natural forest disturbances and post-disturbance salvage logging affect habitat suitability for two ungulates, roe deer (<i>Capreolus capreolus</i>) and red deer (<i>Cervus elaphus</i>), over 32 years in a Central European forest landscape. We found that red and roe deer differed in their response to forest disturbances. Habitat suitability for red deer consistently improved after disturbances, whereas the suitability of disturbed sites was more variable for roe deer depending on season (lower during winter than summer) and disturbance agent (lower in windthrow versus bark-beetle-affected stands). Salvage logging altered the suitability of bark beetle-affected stands for deer, having negative effects on red deer and mixed effects on roe deer, but generally did not have clear effects on habitat suitability in windthrows. Our results highlight long-lasting legacy effects of forest disturbances on deer habitat. For example, bark beetle disturbances improved red deer habitat suitability for at least 25 years. The duration of disturbance impacts generally increased with elevation. Methodologically, our approach proved effective for improving the robustness of habitat reconstructions from Landsat time series: integrating multi-year telemetry data into single, multi-temporal habitat models improved model transferability in time. Likewise, temporally segmenting the Landsat-based metrics increased the temporal consistency of our habitat suitability maps. As the frequency of natural forest disturbances is increasing across the globe, their impacts on wildlife habitat should be considered in wildlife and forest management. Our approach offers a widely applicable method for monitoring habitat suitability changes caused by landscape dynamics such as forest disturbance</p>
Distribution, response to human disturbance, habitat preferences, and acoustic communication of tree hyraxes of Mt. Kilimanjaro, Tanzania
<p><span>This data consists data from recordings done in Mt. Kilimanjaro. Hourly calls of tree hyraxes have been calculated between 19.00 until 06:00. Dataset also has variables collected by other research groups.</span></p> <p><span>We combined our data of cue count per hour with data to analyse tree hyrax density with explanatory variables to model occupancy of tree hyraxes in Kilimanjaro. Dataset was combined from several research projects conducted within the Kili-Project (Hemp et al. 2018) (Table 1). Variables included forest type, temperature (Appelhans et al. 2015) precipitation (Appelhans et al. 2016). diameter breast height (DBH), leaf density, max vegetation height, and leaf area index (LAI) (Rutten et al., 2015). We also included land use index (LUI) (Peters et al. 2019) to the dataset, which included four different variables (percentage plant biomass removal, agricultural inputs, modification of the vegetation and percentage of agricultural area in the surroundings). </span></p> <p><span>Abstract</span></p> <p><span>Limited knowledge exists of the distribution, habitat selection, behavior and response to human disturbance of many mammalian species from mountains of Africa. This is especially true for nocturnal mammals. We studied acoustically very active tree hyraxes (<em>Dendrohyrax validus validus</em>) from Mt. Kilimanjaro National Park, Tanzania mainly with bioacoustical methods. To gain understanding of the habitat preferences of tree hyraxes we combined bioacoustical data with botanical and meteorological data collected earlier by <span>KiLi Project</span>. According to GLMM analysis, disturbance caused by logging or forest fires significantly reduced tree hyrax calling activity. In Mt. Kilimanjaro, highest density of tree hyraxes was found from 2750 m a.s.l. It seems that extensive hunting in the past and selective logging below elevation 2500 m caused tree hyraxes to move up the mountain. Calls of tree hyraxes in Mt. Kilimanjaro resemble calls emitted by hyraxes in Taita Hills, Kenya; however, there are clear differences in their calling cultures. In Mt. Kilimanjaro tree hyraxes also sing songs, and their acoustic communication is very active and diverse. In most preferred habitats, groups of tree hyraxes may call 4500–5500 times during one night. Calling seem to have elements of turn taking and individual signatures. Future of tree hyraxes in large, 650 km<sup>2</sup>, Mt. Kilimanjaro National Park seems promising and perhaps in the future tree hyraxes will recolonize the whole park area again.</span></p>
Effects of disturbance on plant regrowth along snow pack gradients in alpine habitats
<p class="MsoNormal"><span>Human disturbance in alpine habitats is expected to increase, and improved knowledge of short-term recovery<span> </span>after disturbance events is necessary to interpret vegetation responses and formulate planning and mitigation efforts. The ability of a plant community to return to its original state after a disturbance (community resilience) depends on species composition and environmental conditions. The aim of this study is to analyze initial short-term effects of disturbance in alpine plant communities in contrasting climates (oceanic vs. continental; central Norway). We used a nested block-design to examine vegetative regrowth and seedling recruitment after experimental perturbation. Three plant community types along the snow pack gradient were exposed to (1) no disturbance, (2) clipping, and (3) clipping and uprooting. Slow vegetative regrowth and low seedling establishment rates were found in dry alpine ridges and late-melting oceanic snowbed communities. Leeside habitats with intermediate snow conditions were found more resilient. The difference was related to growth form and species diversity. Woody species, which dominated in ridges and oceanic snowbeds, showed the most negative response to disturbance. Species-rich plant communities dominated by graminoids and herbs showed higher rates of regrowth. Species richness seems to cause resilience to the plant communities through higher response diversity. Plant communities at the extreme ends of abiotic gradients, ridges and late-melting snowbeds, will be most sensitive to both disturbance and environmental change. In an up-scaled human-used landscape disturbance effects will be amplified and further limit recovery to a pre-disturbance state.</span></p>
Ecological drivers of avian diversity in a subtropical landscape: effects of habitat diversity, primary productivity and anthropogenic disturbance
<p>Understanding the roles of ecological drivers in shaping biodiversity is fundamental for conservation practice. In this study, we explored the effects of elevation, conservation status, primary productivity, habitat diversity, and anthropogenic disturbance (represented by human population density and birding history) on taxonomic, phylogenetic and functional avian diversity in a subtropical landscape in southeastern China. We conducted bird surveys using 1-km transects across a total of 30 sites, of which 10 sites were located within a natural reserve. Metrics of functional diversity were calculated based on six functional traits (body mass, clutch size, dispersal ratio, sociality, diet and foraging stratum). We built simultaneous autoregression models to assess the association between the ecological factors and diversity of the local avian communities. Local avian diversity generally increased with increasing habitat diversity, human population density and primary productivity. We also detected phylogenetic and functional clustering in these communities, suggesting that the avian assemblages were structured mainly by environmental filtering, rather than interspecific competition. Compared to sites outside the natural reserve, sites within the natural reserve had relatively lower avian diversity but a higher level of phylogenetic heterogeneity.</p>
Data from: Disentangling direct from indirect effects of habitat disturbance on multiple components of biodiversity
<p><span>Human habitat disturbance affects both species diversity and intraspecific genetic diversity, leading to correlations between these two components of biodiversity</span><span> </span><span>(termed species - genetic diversity correlation, SGDC). However, whether</span><span> </span><span>SGDC predictions extend to host-associated communities, such as the intestinal parasite and gut microbial diversity, remains largely unexplored.</span><span> </span><span>Additionally, the role of dominant generalist species is often neglected despite their importance in shaping the environment experienced by other members of the ecological community, and their role as source, reservoir and vector of zoonotic diseases. New analytical approaches (e.g., structural equation modelling, SEM) can be used to assess SGDC relationships and distinguish among direct and indirect effects of habitat characteristics and disturbance on the various components of biodiversity</span><span>.</span></p> <p><span>With six concrete and biologically sound models in mind, we collected habitat characteristics of 22 study sites from four distinct landscapes located</span><span> </span><span>in central Panama. Each landscape differed in the degree of human disturbance and fragmentation measured by several quantitative variables, such as canopy cover, canopy height and understory density.</span><span> </span><span>In terms of biodiversity, we estimated on the one hand, 1) small mammal species diversity, and, on the other hand, 2) genome-wide diversity, 3) intestinal parasite diversity and 4) gut microbial heterogeneity of the most dominant generalist species (Tome's spiny rat, <em>Proechimys semispinosus</em>). We used</span><span> </span><span>SEMs to assess the links between habitat characteristics and biological diversity measures.</span></p> <p><span>The best supported SEM suggested that habitat characteristics directly and positively affect the richness of small mammals, the genetic diversity of <em>P. semispinosus</em> and its gut microbial heterogeneity. Habitat characteristics did not, however, directly impact intestinal parasite diversity.</span><span> </span><span>We</span><span> also </span><span>detected indirect, positive effects of habitat characteristics on both host-associated assemblages via small mammal richness. For microbes, this is likely linked to cross species transmission, particularly in shared and/or anthropogenically altered habitats, whereas host diversity mitigates parasite infections. The SEM revealed an additional indirect but negative effect on </span><span>intestinal</span><span> parasite diversity via host genetic diversity.</span></p> <p><span>Our study </span><span>showcases that habitat alterations not only affect species diversity and host genetic diversity in parallel, but also species diversity of host-associated assemblages. The impacts from </span><span>human disturbance are therefore expected to ripple through entire ecosystems with far reaching effects felt even by generalist species.</span></p>
Temporal patterns of gut microbiota in lemurs (Eulemur rubriventer) living in intact and disturbed habitats.
<p>This data set includes the R scripts (combined into one R markdown document) and input files needed to create the main text figures and major analyses for the paper "Grieneisen L, Hays A, Cook E, Blekhman R, and Tecot S. 2024. Temporal patterns of gut microbiota in lemurs (<em>Eulemur rubriventer</em>) living in intact and disturbed habitats. American Journal of Primatology." </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>
Impacts of habitat disturbance on population health of Bornean frogs
<b>Description: </b><p>The raw data required for estimates of size, body condition and fluctuating asymmetry of riparian anurans across a series of streams within the SAFE landscape, providing insight into the population health of frog populations at these sites. Streams were selected to provide a habitat disturbance gradient enabling identification of any effect of habitat disturbance on the population health indices. Stream characteristics were measured to provide estimates of habitat disturbance sorrounding the streams (canopy cover) and enable the impact of any potentially confounding variables (stream width and slope) to be eliminated. </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/205"><b>The impact of habitat modification on feeding interactions between riparian anuran communities and their arthropod prey </b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3485086">here</a></p><p><b>Files: </b>This consists of 1 file: Fluctuating_Asymmetry_and_Body_Condition_of_Anurans_in_the_SAFE_Project.xlsx</p><p><b>Fluctuating_Asymmetry_and_Body_Condition_of_Anurans_in_the_SAFE_Project.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Fluctuating asymmetry and body condition measurements of Bornean riparian anurans</b> (described in worksheet Frogs)</p><p>Description: Species, snout-vent length, mass and limb measurements of anurans sampled across a tropical forest disturbance gradient and within oil palm plantations. </p><p>Number of fields: 37</p><p>Number of data rows: 240</p><p>Fields: </p><ul><li><b>site</b>: SAFE identification code for the location where sampling was conducted (Field type: location)</li><li><b>date</b>: Date of sampling (Field type: date)</li><li><b>identification_code</b>: Code for the distinction of individual sampled frogs (Site of capture followed by the number of the individual within the overall sample taken within the site) (Field type: id)</li><li><b>species</b>: Identification of sampled frog to species level (Field type: taxa)</li><li><b>sex</b>: Sex of the sampled frog (male, female or unknown) (Field type: categorical trait)</li><li><b>svl</b>: Length of the frog from its snout to its vent (Field type: numeric trait)</li><li><b>mass</b>: Mass of the frog (Field type: numeric trait)</li><li><b>left_ru_1</b>: First measurement of the left radio-ulna of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_ru_2</b>: Second measurement of the left radio-ulna of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_ru_3</b>: Third measurement of the left radio-ulna of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_ru_mean</b>: Average of the three measurements of the left radio-ulna of the frog (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>right_ru_1</b>: First measurement of the right radio-ulna of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_ru_2</b>: Second measurement of the right radio-ulna of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_ru_3</b>: Third measurement of the right radio-ulna of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_ru_mean</b>: Mean of the three measurements of the right radio-ulna of the frog (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>mean_ru_size</b>: Average length of both radio-ulnas (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>ru_diff</b>: Difference between the mean length of the right radio-ulna from all three measurements and that of the left (Field type: numeric trait)</li><li><b>left_thigh_1</b>: First measurement of the left thigh of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_thigh_2</b>: Second measurement of the left thigh of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_thigh_3</b>: Third measurement of the left thigh of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_thigh_mean</b>: Average of the three measurements of the left thigh of the frog (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>right_thigh_1</b>: First measurement of the right thigh of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_thigh_2</b>: Second measurement of the right thigh of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_thigh_3</b>: Third measurement of the right thigh of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_thigh_mean</b>: Mean of the three measurements of the right thigh of the frog (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>mean_thigh_size</b>: Average length of both thighs (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>thigh_diff</b>: Difference between the mean length of the right thigh from all three measurements and that of the left (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>left_tf_1</b>: First measurement of the left tibio-fibula of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_tf_2</b>: Second measurement of the left tibio-fibula of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_tf_3</b>: Third measurement of the left tibio-fibula of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_tf_mean</b>: Mean of the three measurements of the left tibio-fibula of the frog (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>right_tf_1</b>: First measurement of the right tibio-fibula of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_tf_2</b>: Second measurement of the right tibio-fibula of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_tf_</b>: Third measurement of the right tibio-fibula of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_tf_mean</b>: Mean of the three measurements of the right tibio-fibula of the frog (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>mean_tf_length</b>: Average length of both tibio-fibulas (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>tf_diff</b>: Difference between the mean length of the right tibio-fibula from all three measurements and that of the left (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li></ul></li><li><p><b>Stream morphometrics and riparian canopy cover</b> (described in worksheet Streams)</p><p>Description: The width and slope of, as well as the canopy cover above, the streams along which anurans were sampled.</p><p>Number of fields: 10</p><p>Number of data rows: 260</p><p>Fields: </p><ul><li><b>site</b>: SAFE identification code for the location where sampling was conducted (Field type: location)</li><li><b>date</b>: Date of sampling (Field type: date)</li><li><b>point</b>: 20m interval point along 500m in-stream transect at which measurements were taken (Field type: id)</li><li><b>upstream_%_canopy_cover</b>: Percentage canopy coverage when facing upstream from the centre of the stream at the given sampling point (Field type: numeric)</li><li><b>right_%_canopy</b>: Percentage canopy coverage when facing toward the right bank from the centre of the stream at the given sampling point (Field type: numeric)</li><li><b>downstream_%_canopy_cover</b>: Percentage canopy coverage when facing downstream from the centre of the stream at the given sampling point (Field type: numeric)</li><li><b>left_%_canopy_cover</b>: Percentage canopy coverage when facing toward the left bank from the centre of the stream at the given sampling point (Field type: numeric)</li><li><b>average_cover</b>: Average of the four canopy cover measurements (Field type: numeric)</li><li><b>stream_width</b>: Wetted width of the stream (Field type: numeric)</li><li><b>stream_slope</b>: Slope of the stream (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2019-03-23 to 2019-05-16</p><p><b>Latitudinal extent: </b>4.6025 to 4.7317</p><p><b>Longitudinal extent: </b>117.4556 to 117.6414</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> -  -  -  Amphibia <br> -  -  -  -  Anura <br> -  -  -  -  -  Bufonidae <br> -  -  -  -  -  -  <i>Phrynoidis</i> <br> -  -  -  -  -  -  -  <i>Phrynoidis juxtaspera</i> <br> -  -  -  -  -  Dicroglossidae <br> -  -  -  -  -  -  <i>Limnonectes</i> <br> -  -  -  -  -  -  -  <i>Limnonectes finchi</i> <br> -  -  -  -  -  -  -  <i>Limnonectes kuhlii</i> <br> -  -  -  -  -  -  -  <i>Limnonectes leporinus</i> <br> -  -  -  -  -  Rhacophoridae <br> -  -  -  -  -  -  <i>Polypedates</i> <br> -  -  -  -  -  -  -  <i>Polypedates otilophus</i> <br> -  -  -  -  -  Ranidae <br> -  -  -  -  -  -  <i>Chalcorana</i> <br> -  -  -  -  -  -  -  [Chalcorana raniceps] <br> -  -  -  -  -  -  <i>Meristogenys</i> <br> -  -  -  -  -  -  -  [Meristogenys orphnocnemus] <br> -  -  -  -  -  -  <i>Staurois</i> <br> -  -  -  -  -  -  -  <i>Staurois guttatus</i> <br> -  -  -  -  -  -  -  <i>Staurois latopalmatus</i> <br> -  -  -  -  -  Megophryidae <br> -  -  -  -  -  -  <i>Leptolalax</i> <br> -  -  -  -  -  -  -  [Leptolalax friteniens] <br> -  -  -  -  -  -  <i>Leptobrachium</i> <br> -  -  -  -  -  -  -  [Leptobrachium abboti] <br></div><p></p>
Effects of disturbance on plant regrowth along snow pack gradients in alpine habitats
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Breaking ecological barriers: anthropogenic disturbance leads to habitat transitions, hybridization, and high genetic diversity
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Density as a mechanism linking habitat disturbance to increased disease prevalence: evidence from a natural experiment
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Data from: Red pandas on the move: Weather and disturbance effects on habitat specialists
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Ecological drivers of avian diversity in a subtropical landscape: effects of habitat diversity, primary productivity and anthropogenic disturbance
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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
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