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209 results for “Microclimate”

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zenodo40/100

Microclimate in cocoa production systems Data

<p>This dataset contains the canopy openness and light, throughfall, temperature and relative humidity of cocoa monocultures and agroforestry systems within a long-term trial in Alto Beni, Bolivia. The dataset has be described in the Article &quot;Shade trees and tree pruning alter throughfall and microclimate in cocoa (<em>Theobroma cacao</em> L.) production systems&quot; published in Annals of Forest Science. Please cite both, the article and the DOI of the dataset when making use of the data.</p> <p>&nbsp;</p>

opencc-by-4.0Feb 2018View details →
zenodo40/100

Animal and microclimate data of AGROMIX WP3 pilot site trial at Tenuta di Paganico (GR) Italy - (2021 and 2022)

<p>Datasets of collected data on (i) animal weight and average daily gain, (ii) hair cortisol, (iii) blood glucose (iv) and black globe humidity index during the trial conducted in Spring and summer 2021 and 2022 at the AGROMIX trial site of Tenuta di Paganico (GR), Italy.</p>

opencc-by-4.0Oct 2024View details →
zenodo40/100

Microclimate data of Belgian Aelmoeseneie forest in November/December 2020

<p>Microclimate data of Aelmoeseneie forest (Gontrode, Belgium) from two data loggers and open field climate data for one month. This data was used in an MDPI publication.</p>

opencc-by-4.0Jul 2021View details →
dryad40/100

Data from: The experimental manipulation of atmospheric drought: Teasing out the role of microclimate in biodiversity experiments

<p class="MsoNormal">Climate change alters mean global surface temperatures, precipitation regimes, and atmospheric moisture. Resultant drought affects the composition and diversity of terrestrial ecosystems worldwide. To date, there are no assessments of the combined impacts of reduced precipitation and atmospheric drying on functional trait distributions of any species in an outdoor experiment. Here, we examined whether soil and atmospheric drought affect the functional traits of a focal grass species (<em>Poa secunda)</em> growing in monoculture and 8-species grass communities in outdoor mesocosms. We focused on specific leaf area (SLA), leaf area, stomatal density, root:shoot ratio, and fine root:coarse root ratio responses. Leaf area and overall growth were reduced with soil drying. Root:shoot ratio only increased for <em>P. secunda</em> growing in monoculture under combined atmospheric and soil drought. Plant energy allocation strategy (measured using principal components) differed when <em>P. secunda</em> was grown in combined soil and atmospheric drought conditions compared with soil drought alone. Given a lack of outdoor manipulations of this kind, our results emphasize the importance of atmospheric drying on functional trait responses more broadly. We suggest that drought methods focused purely on soil water inputs may be imprecisely predicting drought effects on other terrestrial organisms as well (other plants, arthropods, and higher trophic levels).</p>

opencc-zeroMay 2023View details →
dryad40/100

Data from: Stingless bee foragers experience more thermally stressful microclimates and have wider thermal tolerance breadths than other worker subcastes

Open the record for dataset details and reuse information.

publicMar 2024View details →
dryad40/100

Data for: Microclimate structures communities, predation and herbivory in the High Arctic

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publicDec 2020View details →
dryad40/100

Data from: Macro- and microclimate interactively shape species diversity of multiple taxa in mountain landscapes

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publicOct 2025View details →
dryad40/100

Experimental evidence that nest orientation influences microclimate in a temperate grassland

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publicJan 2024View details →
dryad40/100

Data from: The experimental manipulation of atmospheric drought: Teasing out the role of microclimate in biodiversity experiments

Open the record for dataset details and reuse information.

publicMay 2023View details →
edi40/100

Handheld Microclimate Data from Californian Drylands

Handheld microclimate data (temperature and relative humidity) collected using the Mengshen Temperature and Humidity Meter at two aridity gradients across California. Microsites included the open, shrub, and two artificial shelter types: square and triangle.

openCC (other)Jun 2022View details →
edi40/100

Logger Microclimate Data from Californian Drylands Winter 2023 Sampling.

Logger microclimate data (temperature, sunlight intensity, and relative humidity) were collected using the HOBO ONSET 64K data logger and OMEGA engineering temperature and RH pendant two aridity gradients across California. Microsites included the open, shrub, and two artificial shelter types: square and triangle.

openCC (other)Mar 2023View details →
edi40/100

Handheld Microclimate Data from Californian Drylands 2023.

Handheld microclimate data (temperature and relative humidity) collected using the Mengshen Temperature and Humidity Meter at two aridity gradients across California. Microsites included the open, shrub, and two artificial shelter types: square and triangle. Wind speed was recorded using Kestrel handheld device. Surface Ground Temperature was recorded using a laser meter.

openCC (other)Jun 2023View details →
edi40/100

Sap Flux and Microclimate Data for Co-Occurring White Spruce and Paper Birch at an Intermediate Aged Stand in the Bonanza Creek LTER Regional Site Network 2013-2018

This dataset contains hourly mean sap flux density and microclimate data for co-occurring white spruce and Alaska paper birch from early June of 2013 to mid-September of 2018. The data were published as part of a 2021 article in Journal of Ecology.

openOpenMar 2021View details →
edi40/100

Microclimate Measurements from the Terrestrial Gradient Plots, Coweeta Hydrologic Laboratory, North Carolina

The terrestrial gradient study at Coweeta compares vegetation, soils, and understory microclimate of five sites: 118 low elevation (782 m) pine-oak, 218 low elevation (795 m) cove hardwood, 318 low elevation (865 m) mixed oak, 427 high elevation (1001 m) mixed oak, 527 high elevation (1347 m) northern hardwood. Understory microclimate stations were installed in representative locations at the downslope margin of each 20 x 40 m gradient plot (within the 80 x 80 m plot).

openCustomJan 2020View details →
dryad36/100

Data from: External temperature and distance from nearest entrance influence microclimates of cave and culvert roosting tri-colored bats Perimyotis subflavus

<p>Many North American bat species hibernate in both natural and artificial roosts. Although hibernacula can have high internal climate stability, they still retain spatial variability in their thermal regimes, resulting in various 'microclimates' throughout the roost that differ in their characteristics (e.g., temperature, air moisture). These microclimate components can be influenced by factors such as the number of entrances, the depth of the roost, and distance to the nearest entrance of the roost. Tri-colored bats are commonly found roosting in caves in winter, but they can also be found roosting in large numbers in culverts, providing the unique opportunity to investigate factors influencing microclimates of bats in both natural and artificial roost sites. As tri-colored bats are currently under consideration for federal listing, information of this type could be useful in aiding in the conservation and management of this species through a better understanding of what factors affect the microclimate near roosting bats. We collected data on microclimate temperature and microclimate actual water vapor pressure (AWVP) from a total of 760 overwintering tri-colored bats at 18 caves and 44 culverts.  Using linear mixed models analysis, we found that variation in bat microclimate temperatures was best explained by external temperature and distance from nearest entrance in both caves and culverts. External temperature had a greater influence on microclimate temperatures in culverts than caves. We found that variation in microclimate AWVP was best explained by external temperature, distance from nearest entrance and proportion from entrance (proportion of the total length of the roost from the nearest entrance) in culvert roosting bats. Variation in microclimate AWVP was best explained by external temperature and proportion from entrance in cave roosting bats. Our results suggest that bat microclimate temperature and AWVP are influenced by the similar factors in both artificial and natural roosts, although the relative contribution of these factors differ between roost types.</p>

opencc-zeroNov 2020View details →
zenodo36/100

The Resilience of Tropical Forest Invertebrates to Microclimate Change

<b>Description: </b><p>This dataset examines the thermal physiology of ants accross the SAFE project, with the goal of understanding how changing microclimates affect communities of invertebrates in disturbed landscapes. Tropical invertebrates are expected to already live close to their upper thermal tolerances, and so the rapid changes to microclimate brought about by logging may be a powerful determinant of the emergent communites in disturbed forests. The worksheet contains the upper critical temperature (CTmax) of individual ants identified to genus level. Ants were collected from the ground or soil layer unless specified as arboreal. CTmax was determined using a ramping procedure whereby temeprature was increased from 32 degrees upwards at a rate of 0.2 degrees per minuted until individuals lost motor control. Ants were found by searching opportunistically throughout entire blocks, therefore for locations we have simply inputted one large fractal order from each sampling block used.</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/135"><b>The Resilience of Tropical Forest Invertebrates to Microclimate Change</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=4297673">here</a></p><p><b>Files: </b>This consists of 1 file: MJWB_SAFE_CTmax_Upload.xlsx</p><p><b>MJWB_SAFE_CTmax_Upload.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Ant.CTmax</b> (described in worksheet Ant.CTmax)</p><p>Description: The worksheet contains the upper critical temperature (CTmax in degrees centigrade) of individual ants identified to genus level. Ants were collected from the ground or soil layer unless specified as arboreal. CTmax was determined using a ramping procedure whereby temeprature was increased from 32 degrees upwards at a rate of 0.2 degrees per minuted until individuals lost motor control. Ants were found by searching opportunistically throughout entire blocks, therefore for locations we have simply inputted one large fractal order from each sampling block used.</p><p>Number of fields: 4</p><p>Number of data rows: 2359</p><p>Fields: </p><ul><li><b>CTmax</b>: Critical upper thermal tolerance in degrees centigrade (Field type: numeric)</li><li><b>Genus</b>: Genus name of ant (Field type: taxa)</li><li><b>Location</b>: SAFE Project sampling block (Field type: location)</li><li><b>Arboreal</b>: Comment on if the ant was sampled from the ground or arboreal layer (Field type: comments)</li></ul></li></ol><p><b>Date range: </b>2015-10-01 to 2019-10-01</p><p><b>Latitudinal extent: </b>4.6380 to 4.7412</p><p><b>Longitudinal extent: </b>116.9568 to 117.6245</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>&ensp;-&ensp; Animalia <br>&ensp;-&ensp;&ensp;-&ensp; Arthropoda <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Insecta <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Hymenoptera <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Formicidae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Acanthomyrmex</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Aenictus</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Bothriomyrmex</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Camponotus</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Cardiocondyla</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Carebara</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Cataulacus</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Centromyrmex</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Crematogaster</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Cryptopone</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Diacamma</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Dolichoderus</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Echinopla</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Euprenolepis</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Hypoponera</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Iridomyrmex</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Lepisiota</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Leptogenys</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Lophomyrmex</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Lordomyrma</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Monomorium</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Myrmecina</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Myrmicaria</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Nylanderia</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Ochetellus</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Odontomachus</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Odontoponera</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Oecophylla</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Pachycondyla</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Paraparatrechina</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Paratopula</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Paratrechina</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Pheidole</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Pheidologeton</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Philidris</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Plagiolepis</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Polyrhachis</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Ponera</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Prenolepis</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Prionopelta</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Pristomyrmex</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Pseudolasius</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Rhoptromyrmex</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Rhytidoponera</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Tapinoma</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Technomyrmex</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Tetramorium</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Tetraponera</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Vollenhovia</i> <br></div><p></p>

opencc-by-4.0Dec 2019View details →
dryad36/100

Data from: Examining the microclimate hypothesis in Amazonian birds: indirect tests of the 'visual constraints' mechanism

Proposed mechanisms for the decline of terrestrial and understory insectivorous birds in the tropics include a related subset that together has been termed the "microclimate hypothesis." One prediction from this hypothesis is that sensitivity to bright light environments discourages birds of the dimly lit rainforest interior from using edges, gaps, or disturbed forest. Using a hierarchical Bayesian framework and capture data across time and space, we tested this by first determining vulnerability based on differences in within-species capture rates between disturbed and undisturbed forest for 64 bird species at the Biological Dynamics of Forest Fragments Project in central Amazonian Brazil. We found that 35 species (55%) were vulnerable to anthropogenic habitat degradation, whereas only four (6%) were more commonly captured in degraded forest. To infer visual sensitivity, we then examined two different characters: eye size (maximum pupil diameter) relative to body mass and the initiation time of dawn song, which presumably reflects a species' visual capacity under low light intensities. We predicted that species with large relative eye sizes and birds with earlier dawn songs would exhibit increased vulnerability in degraded habitats with bright light. Contrary to our predictions, however, vulnerability was positively correlated with the mean start time of dawn song. This indicates that species that wait to initiate dawn song are also more vulnerable to habitat degradation. After correcting for body size, there was no effect of eye size on vulnerability. Together, our results do not provide quantitative support for the light sensitivity mechanism of the microclimate hypothesis. More sensitive, experimental tests, such as behavioral assays with controlled light environments, especially in a comparative framework, are needed to rigorously evaluate the role of light sensitivity as an aspect of the microclimate hypothesis among Neotropical birds.

opencc-zeroDec 2018View details →
dryad36/100

Data from: Habitat structure modifies microclimate: an approach for mapping fine-scale thermal refuge

1. Contemporary techniques predicting habitat suitability under climate change projections often underestimate availability of thermal refuges. Habitat structure contributes to thermal heterogeneity at a variety of spatial scales, but quantifying microclimates at organism‐relevant resolutions remains a challenge. Landscapes that appear homogeneous at large scales may offer patchily distributed thermal refuges at finer scales. 2. We quantified the relationship between vegetation structure and the thermal environment at a scale relevant to small, terrestrial animals using a new approach for mapping fine‐scale thermal heterogeneity. We expected that vegetation would create attenuated microclimates and that the influence of vegetation structure would vary seasonally. We measured shrub volume, horizontal cover, and operative temperature (Te) in a sagebrush‐steppe habitat in Idaho, USA, at 534 microsites across two study sites of approximately 1 km2 each. We modeled relationships between habitat structure and both mean daily maximum temperature (urn:x-wiley:2041210X:media:mee313008:mee313008-math-0001max) and mean diurnal temperature range (urn:x-wiley:2041210X:media:mee313008:mee313008-math-0002) for each study site during summer and winter. Aerial imagery from unmanned aerial systems was used to estimate shrub volume and canopy cover at 1‐m resolution, and we applied the best fit model to map thermal heterogeneity across broader extents. 3. Increasing shrub volume and cover was associated with lower urn:x-wiley:2041210X:media:mee313008:mee313008-math-0003max and (urn:x-wiley:2041210X:media:mee313008:mee313008-math-0004, but strengths of the relationships differed between study sites. There was considerable heterogeneity in availability of thermal refuges across sagebrush‐steppe rangelands that have traditionally been considered relatively homogeneous. 4. This technique can help ecologists and land managers identify critical thermal refuges that large‐scale climate modelling can overlook and thus contribute to an understanding of animal‐habitat relationships under changing climates and land uses.

opencc-zeroDec 2017View details →
zenodo36/100

Microclimate Effects and Irrigation Water Requirement of Mesic, Oasis, and Xeric Landscapes

<p>This database provides the model input parameters and simulation files for Mesic, Oasis, and Xeric landscapes.&nbsp;</p>

opencc-by-3.0-usJun 2021View details →
dryad36/100

Data from: Microclimate predicts within-season distribution dynamics of montane forest birds

Aim Climate changes are anticipated to have pervasive negative effects on biodiversity and are expected to necessitate widespread range shifts or contractions. Such projections are based upon the assumptions that (1) species respond primarily to broad-scale climatic regimes, or (2) that variation in climate at fine spatial scales is less relevant at coarse spatial scales. However, in montane forest landscapes, high degrees of microclimate variability could influence occupancy dynamics and distributions of forest species. Using high-resolution bird survey and under-canopy air temperature data, we tested the hypothesis that the high vagility of most forest bird species combined with the heterogeneous thermal regime of mountain landscapes would enable them to adjust initial settlement decisions to track their thermal niches. Location Western Cascade Mountains, Oregon, USA. Methods We used dynamic occupancy models to test the degree to which microclimate affects the distribution patterns of forest birds in a heterogeneous mountain environment. In all models we statistically accounted for vegetation structure, vegetation composition and potential biases due to imperfect detection of birds. We generated spatial predictions of forest bird distributions in relation to microclimate and vegetation structure. Results Fine-scale temperature metrics were strong predictors of bird distributions; effects of temperature on within-season occupancy dynamics were as large or larger (1–1.7 times) than vegetation effects. Most species (86.7%) exhibited apparent within-season occupancy dynamics. However, species were almost as likely to be warm associated (i.e., apparent settlement at warmer sites and/or vacancy at cooler sites; 53.3% of species) as cool associated (i.e., apparent settlement at cooler sites and/or vacancy at warmer sites; 46.7% of species), suggesting that microclimate preferences are species specific. Main conclusions High-resolution temperature data increase the quality of predictions about avian distribution dynamics and should be included in efforts to project future distributions. We hypothesize that microclimate-associated distribution patterns may reflect species' potential for behavioural buffering from climate change in montane forest environments.

opencc-zeroDec 2015View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record