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209 results for “Microclimate”
Data from: Northern pikas experience reduced occupancy due to surrounding human land use despite the occurrence of suitable microclimates
<p>Aim: Despite warming temperatures, some species are found persisting at the trailing edge of their distribution. Microclimates provided by complex topography are considered a key factor in these cases of range stationarity, buffering stress from exposure to warming and enabling persistence. However, for species with trailing-edges located in human-modified landscapes, refugial conditions provided by microclimates could be disrupted by human activities. Here, we aimed to understand the determinants of trailing-edge occupancy for a small lagomorph found in rocky patches harboring cool microclimates.</p> <p>Location: Hokkaido Island, Japan</p> <p>Taxon: Northern pika (<em>Ochotona hyperborea</em>)</p> <p>Methods: We surveyed the occupancy of northern pikas across a wide elevational gradient (350–2200 m) for two consecutive summers. Ambient air and microhabitat (i.e., rock interstices) thermal conditions were measured to assess their relationship. We then analyzed their effects on occupancy at two nested spatial scales: (1) whole-distribution, and (2) at identified trailing-edge sites where we explored the effects of microclimates and surrounding human activities (i,e., distance to nearest road and area of human land use such as plantation forests or agricultural fields).</p> <p>Results: Overall, rock interstices exhibited cooler conditions than ambient air with temperature differences of 1–2 ºC. The overall distribution of northern pikas was affected by both mean ambient temperature and microhabitat availability, with warmer (lower elevation) sites with less microhabitats corresponding to the trailing edge of its distribution. Interestingly, trailing edge occupancy patterns were best explained by the negative effect of surrounding human land despite the existence of suitable microclimates in the rocky patches.</p> <p>Main conclusions: Our findings suggest that the local refugial conditions supported by cool microclimates are likely to be disrupted by the effects of human land at the larger landscape scale. This result highlights the importance of considering the effects of human activities and landscape alteration for effective microrefugia conservation. --</p>
Data from: Effects of microclimate on disease prevalence across an urbanization gradient
<p>Increased temperatures associated with urbanization (the "urban heat island" effect) have been shown to impact a wide range of traits across diverse taxa. At the same time, climatic conditions vary at fine spatial scales within habitats due to factors including shade from shrubs, trees, and built structures. Patches of shade may function as microclimate refugia that allow species to occur in habitats where high temperatures and/or exposure to ultraviolet radiation would otherwise be prohibitive. However, the importance of shaded microhabitats for interactions between species across urbanized landscapes remains poorly understood. Weedy plants and their foliar pathogens are a tractable system for studying how multiple scales of climatic variation influence infection prevalence. Powdery mildew pathogens are particularly well suited to this work, as these fungi can be visibly diagnosed on leaf surfaces. We studied the effects of shaded microclimates on rates of powdery mildew infection on <em>Plantago</em> host species in (1) "pandemic pivot" surveys in which undergraduate students recorded shade and infection status of thousands of plants along road verges in urban and suburban residential neighborhoods, (2) monthly surveys of plant populations in 22 parks along an urbanization gradient, and (3) a manipulative field experiment directly testing effects of shade on growth and transmission of powdery mildew. Together, our field survey results show strong positive effects of shade on mildew infection in wild <em>Plantago</em> populations across urban, suburban, and rural habitats. Our experiment suggests that this relationship is causal, where microclimate conditions associated with shade promote pathogen growth. Overall, infection prevalence increased with urbanization despite a negative association between urbanization and tree cover at the landscape scale. These findings highlight the importance of taking microclimate heterogeneity into account when establishing links between macroclimate or land use context and the prevalence of disease.</p>
Impact of modified caneberry trellis systems on microclimate and habitat suitability for Drosophila suzukii
<p>Caneberries are trellised to facilitate harvest and agrochemical applications as well as to improve crop yield and quality. Trellising can also increase airflow and light penetration within the canopy and affect its microclimate. We compared an experimental trellis that split the canopy into halves to standard I- and V-trellises, measuring <i>Drosophila suzukii</i> (Matsumura) fruit infestation as well as canopy temperature and relative humidity in raspberries at two commercial you-pick diversified farms. To evaluate the combined effects of trellising systems and pruning, we pruned one half of each row in blackberry plantings at two research farms and assessed <i>D. suzukii</i> infestation, canopy microclimate (temperature, relative humidity, and light intensity), fruit quality parameters (interior temperature, total soluble solids, and penetration force), and spray coverage/deposition. Trellis installation costs, labor inputs, and yield were used to further evaluate the trellis systems from an economic perspective. Fruit quality was not affected by trellising or pruning and lower total yield was observed in the experimental trellis treatment on one farm. Although <i>D. suzukii</i> infestation was only affected by trellising and pruning at one site, we observed a relationship between higher temperatures and reduced infestation on nearly all farms. Occasionally, lower relative humidity and high light intensity corresponded with lower infestation. Ultimately, the experimental trellis was less economically efficient than other trellising systems and our ability to successfully manipulate habitat favorability varied in a site-specific manner. <i>Drosophila suzukii </i>management approaches that rely upon unfavorable conditions are likely to be more effective in hot, dry regions.</p>
Supporting data for identifying microclimate tree seedling refugia in post-wildfire landscapes
<p>High-severity wildfire in arid regions has caused ecological state change, transforming previously forested areas into shrublands. This dramatically alters the climatic envelope for tree seedlings, rendering the likelihood of returning post-wildlife landscapes to their previous state relatively low. We used a combination of sUAS imagery, satellite data and in-situ microclimate data recordings, together with a machine learning approach, to model monthly near-ground minimum, mean and max temperature as well as relative humidity and vapor pressure deficit in a previously forested area, which is now dominated by shrubs species. Spatially explicit models predicted recorded microclimate well (<i>r</i> = <span>0.73 to 0.97), and projections of models highlighted the solar buffering capacity of existing vegetation to alter the maximum temperature in the hottest month by ~12<sup>o</sup>C, increase relative humidity by ~20% and reduced vapor pressure deficit by 0.3mbar in locations 2m apart. By harnessing these microclimate refugia, the success rate of reforestation efforts in post-wildfire landscapes could be substantially increased and mitigate seedlings from climate warming at local scales.</span></p>
Supporting data for: Post-fire early successional vegetation buffers surface microclimate and increases survival of planted conifer seedlings in the southwestern United States
<p>Climate change and fire-exclusion have increased the flammability of western US forests, leading to forest cover loss when wildfires occur under severe weather conditions. Increasingly large high-severity burn patches are a limitation to natural regeneration because of dispersal distance, increasing the chance that these areas are converted to non-forest. Post-fire planting can overcome dispersal limitations, yet warmer and drier post-fire conditions can still limit survival. Early successional vegetation can alter surface microclimate; however, it is unclear whether this is enough to increase planted seedling survival in southwestern US forests. Here we examined how two shrub species of different canopy density would affect survival rates of planted tree seedlings following a high-severity fire in northern New Mexico. We expected that shrubs with a higher density canopy (Gambel oak) would have a greater effect on buffering below-shrub climate than shrubs with a lower density canopy (New Mexico locust) and seedlings planted under Gambel oak would have higher survival rates. We found that seedlings planted under Gambel oak had survival rates approximately 10% to 35% greater than those planted under New Mexico locust. The higher light availability beneath New Mexico locust corresponded to higher temperatures, lower humidity, and higher VPD, which impacted the mortality of planted tree seedlings. These results suggest that by waiting for post-fire shrub establishment, shrubs can be leveraged to buffer microclimate and increase post-fire planting success in the southwestern US.</p>
Microclimate-driven trends in spring-emergence phenology in a temperate reptile (Vipera berus): Evidence for a potential 'climate trap'?
<p>Climate change will increase the exposure of organisms to higher temperatures, but can also drive phenological shifts that alter their susceptibility to conditions at the onset of breeding cycles. Organisms rely on climatic cues to time annual life-cycle events, but the extent to which climate change has altered cue reliability remains unclear. Here, we examine the risk of a 'climate trap' – a climatically-driven desynchronisation of the cues that determine life-cycle events and fitness later in the season in a temperate reptile, the European adder (<em>Vipera berus)</em>. During the winter, adders hibernate underground, buffered against sub-zero temperatures, and re-emerge in the spring to reproduce. We derived annual spring-emergence trends between 1983 and 2017 from historical observations in Cornwall, United Kingdom, and related these trends to the microclimatic conditions that adders experienced. Using a mechanistic microclimate model, estimates of below- and near-ground temperatures were used to derive accumulated degree-hour and absolute temperature thresholds that predicted annual spring-emergence timing. Trends in annual emergence timing and subsequent exposure to ground frost were then quantified. We found that adders have advanced their phenology towards earlier emergence. Earlier emergence was associated with increased exposure to ground frost and, contradicting the expected effects of macroclimate warming,<em> </em>increased post-emergence exposure to ground frost at some locations. The susceptibility of adders to this 'climate trap' was related to the rate at which frost risk diminishes relative to advancement in phenology, which depends on the seasonality of climate. We emphasise the need to consider exposure to changing microclimatic conditions when forecasting biological impacts of climate change.</p>
Microclimate and host body condition influence mite population growth in a wild bird-ectoparasite system
<p>Parasite populations are never evenly distributed among the hosts they infect. Avian nest ectoparasites, such as mites, are no exception, as their distribution across the landscape is highly aggregated. It remains unclear if this pattern is driven by differences in transmission events alone, or if the environment that parasites inhabit after transmission also plays a role. Here, we experimentally examined the influence of the post-transmission microclimate, nest characteristics, and host condition on ectoparasite population growth in a bird-ectoparasite system. We infested barn swallow (Hirundo rustica erythrogaster) nests with a standardized number of Northern Fowl Mites (Ornithonyssus sylvarium) and analyzed both biotic (nestling mass, wing length, number of other arthropods present in the nest, and brood size) and abiotic (temperature, humidity, nest lining, nest dimensions, and substrate upon which the nest was built) predictors of mite population growth. Our results suggest that mite populations were most successful, in terms of growth, in nests with higher temperatures, lower humidity, few other arthropods, and hosts in good condition. We also found that nests built on wooden substrates support larger populations of mites than those constructed on metal or concrete. These findings lend insight into the factors that drive large-scale patterns of ectoparasite distributions.</p>
Data for: Microclimate shifts in nest-boxes and natural cavities throughout reproduction
<p>Animals breeding in nest-boxes experience nesting environments in which they did not originally evolve. Despite the central importance of nesting microclimate for offspring fitness, little is known about the thermal properties of human-provided nest sites compared to natural ones. In particular, comparisons with offspring in the nest are lacking. Here, we compare microclimate (temperature and absolute humidity) from the onset of breeding, thus starting with nest-site choice and ending with the post-fledging stage, quantified in natural cavities and nest-boxes used by several species of hollow-nesting birds in a temperate deciduous forest. We confirm that across all nesting stages, nest-boxes were thermally unstable when compared to natural cavities, with higher temperature maximums, larger amplitudes and worse insulation from maximum ambient temperatures relative to natural cavities. Surprisingly, as average humidity of natural cavities was previously shown to be higher than in nest-boxes, in the presence of actively thermoregulating young, nest-boxes were more humid than natural cavities. When offspring were in the nest, internal microclimatic shifts were mitigated three times more effectively in natural cavities than in nest-boxes (in terms of mean daily differences from ambient temperature). Artificial cavity microclimate is likely to amplify the adverse effects of projected temperature increases by compromising thermoregulation of developing animals. We stress that conservation efforts should focus on the protection of areas offering natural breeding-hollows to reduce the potential impacts of climate change on breeding animals.</p>
Data from: Widespread plant species temporal variation along environmental and microclimate gradients in Norwegian mountains
<p>The effect of climate change on mountain vegetation is influenced by environmental factors and site effects. To monitor the effect of climate change we therefore need to understand species sensitivity to microclimate and environmental gradients. The objective of this study is to study widespread plant species temporal and spatial variation along environmental and microclimate gradients in Norwegian mountains along a coast-inland gradient. Occurrence and abundance of plant species were surveyed in 110 study plots in four mountains at two points in time, seven years apart. Of the 222 plant species registered, <em>Salix herbacea, Phyllodoce caerulea, Carex bigelowii, Juncus trifidus, Vaccinium myrtillus, Avenella flexuosa</em>, and <em>Empetrum nigrum</em> were widespread across all mountains. These species responded differently to environmental and microclimate gradients, and abundance data was more sensitive than occurrence data. During the short time span we observed some indications of response which might support the assumption that boreal species outcompete alpine species in the forest transition zone, but our data does not indicate this effect at higher altitudes. Monitoring of climate change in mountains need to include plots along environmental and microclimate gradients as well as abundance of a set of wide-spread plant species that represent both regional and local environmental and climate gradients. However, when monitoring perennial plant species, the necessity of long-time monitoring projects is high because such species develop slowly over several decades.</p>
Data and Script used in "Effects of canopy gaps on microclimate, soil biological activity and their relationship in a European mixed floodplain forest"
<p>The R code and data provided in this repository allow to reproduce the data carpentry, analysis and visualization of “Effects of canopy gaps on microclimate, soil biological activity and their relationship in a European mixed floodplain forest” (https://doi.org/10.1016/j.scitotenv.2024.173572).</p> <p> </p> <p>Folder structure</p> <p> </p> <p>Data abstracts:</p> <p>Data_abstract_climate.pdf</p> <p>Data_abstract_soil_biotics.pdf</p> <p>Data_abstract_soil_abiotics_openness.pdf</p> <p> </p> <p>Data:</p> <p>Climate_data.xlsx</p> <p>Soil_biotics.xlsx</p> <p>Soil_abiotics_openness.xlsx</p> <p> </p> <p>R Scripts:</p> <p>00-preamble.R loads all required packages</p> <p>01-data-carpentry.R loads all datasets and prepares the analysis of all experimental periods.</p> <p>02-data-analyses-microclimate.R compares understorey air and soil microclimate between forest types and treatments, presents diurnal and seasonal variations and tests the relationship of under- and overstorey openness on microclimate.</p> <p>03-data-analyses-decomposition.R compares decomposition rates and feeding activity between forest types and treatments and models the dependencies of soil biological activity on microclimate and soil abiotic factors.</p> <p> </p> <p>Information of related software and package versions used in the script:<br>R version 4.3.2 (2023-10-31 ucrt)<br>Platform: x86_64-w64-mingw32/x64 (64-bit)<br>Running under: Windows 10 x64 (build 19045)<br>Matrix products: default</p> <p> </p> <p>Contact</p> <p>Please contact me at annalena.lenk@uni-leipzig.de if you have further questions.</p>
High temporal resolution microclimate records
<b>Description: </b><p>Microclimate records collected at very high temporal resolution (10 sec) at a small number of sites</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/111"><b>Microclimate stratification in modified forests</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=52">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>High temporal resolution microclimate records</b> (Worksheet Data)</p><p>Dimensions: 229330 rows by 7 columns</p><p>Description: Microclimate records collected at very high temporal resolution (10 sec) at a small number of sites</p><p>Fields: </p><ul><li><b>Plot</b>: Location of record (Field type: Location)</li><li><b>time</b>: Date and time of record (Field type: Datetime)</li><li><b>Temp</b>: Air temperature 1 m above ground (Field type: Numeric)</li><li><b>RH</b>: Relative humidity. (Field type: Numeric)</li><li><b>LoggerType</b>: Make of datalogger that was used (Lascar or iButton) (Field type: ID)</li><li><b>LoggerID</b>: Unique reference number for the datalogger (Field type: ID)</li></ul><br></li></ol><p><b>Date range: </b>2015-12-11 to 2016-12-07</p><p><b>Latitudinal extent: </b>4.7104 to 4.7523</p><p><b>Longitudinal extent: </b>116.9484 to 117.6278</p>
Forest microclimate data from 1st order sites
<b>Description: </b><p>Microclimate records collected from SAFE Project 1st order sites in 2011 and 2012</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/111"><b>Microclimate stratification in modified forests</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=88">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>Forest microclimate data from 1st order sites</b> (Worksheet Data)</p><p>Dimensions: 480872 rows by 7 columns</p><p>Description: Microclimate records collected from SAFE Project 1st order sites in 2011 and 2012</p><p>Fields: </p><ul><li><b>Plot</b>: Location of record (Field type: Location)</li><li><b>time</b>: Date and time of record (Field type: Datetime)</li><li><b>Temp</b>: Air temperature 1 m above ground (Field type: Numeric)</li><li><b>RH</b>: Relative humidity. (Field type: Numeric)</li><li><b>LoggerType</b>: Make of datalogger that was used (Lascar or iButton) (Field type: ID)</li><li><b>LoggerID</b>: Unique reference number for the datalogger (Field type: ID)</li></ul><br></li></ol><p><b>Date range: </b>2011-09-01 to 2012-06-01</p><p><b>Latitudinal extent: </b>4.6350 to 4.7702</p><p><b>Longitudinal extent: </b>116.9477 to 117.7020</p>
Microclimate change, forest disturbance and twig-dwelling ants
<b>Description: </b><p>Data and twig colonisation experiments on twig-nesting ant communities</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/33"><b>Microclimate change, forest disturbance and twig-dwelling ants</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=74">here</a></p><p><b>Data worksheets: </b>There are 6 data worksheets in this dataset:</p><ol><li><p><b>Plot locality information</b> (Worksheet Plot.Locations)</p><p>Dimensions: 28 rows by 5 columns</p><p>Description: Identifies the nearest SAFE Project sampling site to survey plots</p><p>Fields: </p><ul><li><b>Forest.area </b>: SAFE Project Block in which sampling took place (Field type: ID)</li><li><b>Plot</b>: Plot within Forest.area (Field type: ID)</li><li><b>Location</b>: SAFE Project sample site (Field type: Location)</li><li><b>SAFE.Code</b>: SAFE Project sample site (Field type: ID)</li></ul><br></li><li><p><b>Twig-nesting ant communities</b> (Worksheet Observed.Colonies)</p><p>Dimensions: 154 rows by 10 columns</p><p>Description: Plot surveys of naturally occurring twig-nesting ant communities</p><p>Fields: </p><ul><li><b>Location</b>: SAFE project sampling point (Field type: Location)</li><li><b>Forest.area</b>: SAFE Project Block in which sampling took place (Field type: ID)</li><li><b>Plot</b>: Plot within Forest.area (Field type: ID)</li><li><b>SAFE.Code</b>: SAFE project sampling point (Field type: ID)</li><li><b>Subplot</b>: Sub-plot within Plot (Field type: ID)</li><li><b>Genus</b>: Genus identity (Field type: Taxa)</li><li><b>Date</b>: Day on which the survey took place (Field type: Date)</li><li><b>No.Searched</b>: The number of twigs examined for ant colonies in the 1m sub-plot quadrat. Fifteen person-minutes of searching time was put in at each sub-plot to search for colonies of twig-dwelling ants. To locate potential colonies, all twigs of up to 4 cm in diameter found within the search time were broken open to detect any ants living in twig cavities. (Field type: Numeric)</li><li><b>No.Colonies.Found</b>: The number of colonies found in the 1m sub-plot quadrat, during the searching time. A colony was defined as by Sagata et al. (2010) as a group of ants with at least one queen, or at least two workers with brood. (Field type: Numeric)</li></ul><br></li><li><p><b>Experimental colonisation of twigs</b> (Worksheet Twig.Opening)</p><p>Dimensions: 905 rows by 14 columns</p><p>Description: Observations of twig-ant colonisation of experimental twigs</p><p>Fields: </p><ul><li><b>Location</b>: SAFE project sampling point (Field type: Location)</li><li><b>Forest.area</b>: SAFE Project Block in which sampling took place (Field type: ID)</li><li><b>Plot</b>: Plot within Forest.area (Field type: ID)</li><li><b>SAFE.Code</b>: SAFE project sampling point (Field type: ID)</li><li><b>Subplot</b>: Sub-plot within Plot (Field type: ID)</li><li><b>Twig</b>: The identification number of an individual artificial twig (Field type: ID)</li><li><b>Date.out</b>: The date the individual artificial twig was placed in the subplo (Field type: Date)</li><li><b>Date.check</b>: The date on which the artificial twig was watched to deterimine whether it was occupied (Field type: Date)</li><li><b>Prediction</b>: Was the twig predicted to be occupied by ants based on visual observation? (Field type: Categorical)</li><li><b>Open.results</b>: Is an ant colonYpresent in the twig? (Field type: Categorical)</li><li><b>Genus</b>: Genus identity (Field type: Taxa)</li><li><b>Termites</b>: Did the twig have termites inside when cut open? (Field type: Categorical)</li><li><b>Comments</b>: Comments on problems, or weather if it may have affected the prediction. (Field type: Comments)</li></ul><br></li><li><p><b>Environmental data</b> (Worksheet Environ.Data)</p><p>Dimensions: 943 rows by 18 columns</p><p>Description: Microclimate conditions at each of the survey plots</p><p>Fields: </p><ul><li><b>Location</b>: SAFE project sampling point (Field type: Location)</li><li><b>Forest.area</b>: SAFE Project Block in which sampling took place (Field type: ID)</li><li><b>Plot</b>: Plot within Forest.area (Field type: ID)</li><li><b>SAFE.Code</b>: SAFE project sampling point (Field type: ID)</li><li><b>Subplot</b>: Sub-plot within Plot (Field type: ID)</li><li><b>Date</b>: Date the data was collected (Field type: Date)</li><li><b>Time</b>: Time the data was collected (Field type: Time)</li><li><b>Tair </b>: Air temperatures (Field type: Numeric)</li><li><b>Tsoil</b>: Soil temperature (Field type: Numeric)</li><li><b>Hum</b>: Humidity (Field type: Numeric)</li><li><b>Lux</b>: Light level (Field type: Numeric)</li><li><b>Tsurf1</b>: Surface temperature (one of four measurements in the plot) (Field type: Numeric)</li><li><b>Tsurf2</b>: Surface temperature (one of four measurements in the plot) (Field type: Numeric)</li><li><b>Tsurf3</b>: Surface temperature (one of four measurements in the plot) (Field type: Numeric)</li><li><b>Tsurf4</b>: Surface temperature (one of four measurements in the plot) (Field type: Numeric)</li><li><b>Sun</b>: An estimate of the amount of direct sunlight on the ground of the subplot. (Field type: Numeric)</li><li><b>Comments</b>: Observations about weather conditions impacting environmental measurements (Field type: Comments)</li></ul><br></li><li><p><b>Colonisation observations</b> (Worksheet Colonisation)</p><p>Dimensions: 14489 rows by 11 columns</p><p>Description: Visual observation of colonisation status of experimental twigs</p><p>Fields: </p><ul><li><b>Location</b>: SAFE project sampling point (Field type: Location)</li><li><b>Forest.area</b>: SAFE Project Block in which sampling took place (Field type: ID)</li><li><b>Plot</b>: Plot within Forest.area (Field type: ID)</li><li><b>SAFE.Code</b>: SAFE project sampling point (Field type: ID)</li><li><b>Subplot</b>: Sub-plot within Plot (Field type: ID)</li><li><b>Twig</b>: The identification number of an individual artificial twig (Field type: ID)</li><li><b>Date.out</b>: The date the individual artificial twig was placed in the subplo (Field type: Date)</li><li><b>Date.check</b>: The date on which the artificial twig was watched to deterimine whether it was occupied (Field type: Date)</li><li><b>Status</b>: Was the twig predicted to be occupied by ants based oNvisual observation? (Field type: Categorical)</li><li><b>Comments</b>: Additional comments on the state of the twig, if termites were seen, or if there were weather conditions which may have interfered with the survey. (Field type: Comments)</li></ul><br></li><li><p><b>Ant thermotolerance</b> (Worksheet Boiling.Data)</p><p>Dimensions: 605 rows by 4 columns</p><p>Description: Experimental tests of ant thermotolerance</p><p>Fields: </p><ul><li><b>Genus</b>: Genus identity (Field type: Taxa)</li><li><b>T.Max</b>: The temperature at which the ant lost motor control and either crunched up and became still/died, or fell over and began jerking uncontrollably, without recovery. (Field type: Numeric)</li><li><b>Comments</b>: A description of features of the ant if not a worker, of anything that went wrong during the experiement, and of whether the ant was found alive following exposure to T.Max. (Field type: Comments)</li></ul><br></li></ol><p><b>Date range: </b>2015-04-07 to 2016-06-15</p><p><b>Latitudinal extent: </b>4.6373 to 4.7116</p><p><b>Longitudinal extent: </b>117.4523 to 117.6242</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> - Arthropoda<br> -  - Insecta<br> -  -  - Hymenoptera<br> -  -  -  - Formicidae<br> -  -  -  -  - <i>Acanthomyrmex</i><br> -  -  -  -  - <i>Bothriomyrmex</i><br> -  -  -  -  - <i>Camponotus</i><br> -  -  -  -  - <i>Cardiocondyla</i><br> -  -  -  -  - <i>Carebara</i><br> -  -  -  -  - <i>Cataulacus</i><br> -  -  -  -  - <i>Centromyrmex</i><br> -  -  -  -  - <i>Cerapachys</i><br> -  -  -  -  - <i>Crematogaster</i><br> -  -  -  -  - <i>Echinopla</i><br> -  -  -  -  - <i>Forelophilus</i><br> -  -  -  -  - <i>Hypoponera</i><br> -  -  -  -  - <i>Lepisiota</i><br> -  -  -  -  - <i>Meranoplus</i><br> -  -  -  -  - <i>Nylanderia</i><br> -  -  -  -  - <i>Pachycondyla</i><br> -  -  -  -  - <i>Paraparatrechina</i><br> -  -  -  -  - <i>Pheidole</i><br> -  -  -  -  - <i>Pheidologeton</i><br> -  -  -  -  - <i>Plagiolepis</i><br> -  -  -  -  - <i>Polyrhachis</i><br> -  -  -  -  - <i>Prenolepis</i><br> -  -  -  -  - <i>Pseudolasius</i><br> -  -  -  -  - <i>Rhoptromyrmex</i><br> -  -  -  -  - <i>Strumigenys</i><br> -  -  -  -  - <i>Tetramorium</i><br> -  -  -  -  - <i>Tetraponera</i><br> -  -  -  -  - [VT4]<br> -  -  -  -  - <i>Vollenhovia</i><br></div><p></p>
Postojna Planina CS microclimate data eLTER VA
<p>Cave microclimate is prone to important changes in certain passages due to intensive tourism. Monitoring the climatic parameters (air temperature, CO2) in Postojna-Planina Cave System helps to have a proper picture of the impact of tourism for the cave. It is very useful to compare if additional increase of temperature is associated with the changes at the surface climate. The dataset comprises two types of daily mean air temperature data: 1) the daily mean air temperature data (°C) measured in “Lepe jame” cave passage from Postojna Cave, counting constantly a very high number of tourists; the measurements were made in the same point, by three sensors placed at three different height points (in the wall cracks [T1], at ceiling level [T3], and at 2 m height [T2]). Values are recorded at 10 min intervals and then averaged for daily data (the dataset for Lepe jame cave passage is provided by MEIS Environmental consulting d.o.o. (http://www.meis.si/) while the data are part of the project "Assesment of natural and antropogenic processes in micro-meteorology of Postojna cave system by numerical models and modern methods of data aquisition and transfer" [https://izrk.zrc-sazu.si/en/programi-in-projekti/assesment-of-natural-and-antropogenic-processes-in-micrometeorology-of-postojna#v]; 2) the surface daily mean air temperature measured at 2 m height (°C) at the Postojna meteorological station, as part of the national network of meteorological stations within the Environmental Agency of the Republic of Slovenia (ARSO; http://meteo.arso.gov.si/).</p>
Microclimate and the development rate of mosquito vectors
<b>Description: </b><p>This data sets includes microclimate data, mosquito development rate and mosquito wing size measurements collected from primary forest, logged forest and oil palm plantations. Microclimate data was recorded using Ibutton data loggers which measured soil temeprature at given sample sites. All mosquito eggs collected were reared under field conditions. Each mosquito sample was monitored daily in order to record the proportion emerging at each developemnt stage (larva, pupa and adult) along with the numebr of transition days between each development stage. Adult wing length (of each adult mosquito collected) was used as a simple proxy to measure adult vectorial capacity. </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/21"><b>The impact of altered forest microclimate on the development rate of mosquito vectors</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=110">here</a></p><p><b>Files: </b>This consists of 1 file: template_PsomosMosquitoes.xlsx</p><p><b>template_PsomosMosquitoes.xlsx</b></p><p>This file contains dataset metadata and 3 data tables:</p><ol><li><p><b>Soil temperature</b> (described in worksheet Soiltemp)</p><p>Description: Datalogger records of soil temperature time series as sample sites</p><p>Number of fields: 4</p><p>Number of data rows: 875</p><p>Fields: </p><ul><li><b>Site</b>: SAFE Project sample site (Field type: Location)</li><li><b>Day</b>: Day of measurement; each site had records collected over seven days (Field type: Numeric)</li><li><b>Time</b>: Time of measurement (Field type: Time)</li><li><b>Soil Temperature</b>: Soil temperature (Field type: Numeric)</li></ul></li><li><p><b>Mosquito size</b> (described in worksheet Mosquito_wing_length)</p><p>Description: Wing measurements on individual mosquitoes</p><p>Number of fields: 3</p><p>Number of data rows: 119</p><p>Fields: </p><ul><li><b>Site</b>: SAFE Project sample site (Field type: Location)</li><li><b>SampleNumber</b>: Each replicate within a site represents a different mosquito that was measured (Field type: Replicate)</li><li><b>WingSize</b>: Adult mosquito wing length (Field type: Numeric Trait)</li></ul></li><li><p><b>Mosquito life history stages</b> (described in worksheet Development_data)</p><p>Description: Abundance and time frame for mosquito development</p><p>Number of fields: 8</p><p>Number of data rows: 26</p><p>Fields: </p><ul><li><b>Site</b>: SAFE Project sample site (Field type: Location)</li><li><b>Eggs</b>: Number eggs (Field type: Abundance)</li><li><b>Larvae</b>: Number larvae (Field type: Abundance)</li><li><b>Pupae</b>: Number pupae (Field type: Abundance)</li><li><b>Adults</b>: Number adults (Field type: Abundance)</li><li><b>egg-larvae</b>: Number of days to develop from egg to larvae (Field type: Numeric Trait)</li><li><b>larvae-pupae</b>: Number of days to develop from larvae to pupae (Field type: Numeric Trait)</li><li><b>pupae-adult</b>: Number of days to develop from pupae to adult (Field type: Numeric Trait)</li></ul></li></ol><p><b>Date range: </b>2015-05-01 to 2015-07-13</p><p><b>Latitudinal extent: </b>4.6532 to 4.7520</p><p><b>Longitudinal extent: </b>116.9635 to 117.5932</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> - Arthropoda<br> -  - Insecta<br> -  -  - Diptera<br> -  -  -  - Culicidae<br></div><p></p>
Microclimate stratification in modified forests
<b>Description: </b><p>Microclimate datalogger recordings along a gradient from forest floor to forest canopy</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/111"><b>Microclimate stratification in modified forests</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=102">here</a></p><p><b>Files: </b>This consists of 1 file: template_HardwickVerticalStratification.xlsx</p><p><b>template_HardwickVerticalStratification.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Site information</b> (described in worksheet SitesGuide)</p><p>Description: Location of mciroclimate monitoring sites in relation to nearest forest edge</p><p>Number of fields: 3</p><p>Number of data rows: 12</p><p>Fields: </p><ul><li><b>Name</b>: Site of the measurement (Field type: Location)</li><li><b>EdgeDistance</b>: Distance of site from nearest edge. NB: sites with '200m' are a minimum, estimated distance - sites may have been further way; these sites are referred to as 'CORE' and were used as control sites for analysis (Field type: Numeric)</li><li><b>TreeHeight</b>: Height of the tree (Field type: Numeric)</li></ul></li><li><p><b>Microclimate stratification</b> (described in worksheet microclimate)</p><p>Description: Microclimate datalogger recordings along a gradient from forest floor to forest canopy</p><p>Number of fields: 8</p><p>Number of data rows: 290605</p><p>Fields: </p><ul><li><b>Plot</b>: Site of microclimate measurement (Field type: Location)</li><li><b>Height</b>: Height above ground. NB: Negative values indicate belowground sites (Field type: Numeric)</li><li><b>TreeTop</b>: Top of tree canopy? (Field type: Categorical)</li><li><b>time</b>: Date and time of microclimate reading (Field type: Datetime)</li><li><b>Temp</b>: Temperature (Field type: Numeric)</li><li><b>RH</b>: Relative humidity (Field type: Numeric)</li><li><b>LoggerType</b>: Brand of datalogger being used (Field type: Categorical)</li><li><b>LoggerID</b>: Serial number of the datalogger used to record microclimate (Field type: ID)</li></ul></li></ol><p><b>Date range: </b>2013-06-22 to 2015-10-28</p><p><b>Latitudinal extent: </b>4.6837 to 4.6839</p><p><b>Longitudinal extent: </b>117.5859 to 117.5863</p>
Altitudinal gradient of microclimate
<b>Description: </b><p>Microclimate records collected along a gradient of altitude</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/111"><b>Microclimate stratification in modified forests</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=116">here</a></p><p><b>Files: </b>This consists of 1 file: template-AltitudinalMicroclimate.xlsx</p><p><b>template-AltitudinalMicroclimate.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Elevation of study sites</b> (described in worksheet SiteElevation)</p><p>Description: Elevation of study sites</p><p>Number of fields: 2</p><p>Number of data rows: 12</p><p>Fields: </p><ul><li><b>Plot</b>: Location of record (Field type: Location)</li><li><b>Elevation</b>: Altitude of site (Field type: Numeric)</li></ul></li><li><p><b>Altitudinal gradient of microclimate</b> (described in worksheet Data)</p><p>Description: Microclimate records collected along a gradient of altitude</p><p>Number of fields: 6</p><p>Number of data rows: 124901</p><p>Fields: </p><ul><li><b>Plot</b>: Location of record (Field type: Location)</li><li><b>time</b>: Date and time of record (Field type: Datetime)</li><li><b>Temp</b>: Air temperature 1 m above ground (Field type: Numeric)</li><li><b>RH</b>: Relative humidity. (Field type: Numeric)</li><li><b>LoggerType</b>: Make of datalogger that was used (Lascar or iButton) (Field type: ID)</li><li><b>LoggerID</b>: Unique reference number for the datalogger (Field type: ID)</li></ul></li></ol><p><b>Date range: </b>2013-06-06 to 2017-05-10</p><p><b>Latitudinal extent: </b>4.6740 to 4.6858</p><p><b>Longitudinal extent: </b>117.5357 to 117.5532</p>
Microclimate proxy measurements from a logging gradient in Malaysian Borneo (BALI project)
<b>Description: </b><p>Overview<br>We focused our empirical measurements in Sabah, Borneo. We measured a microclimate thermal proxy sensitive to radiative, convective, and conductive heat fluxes. This thermal proxy is relevant to a range of organismal processes including plant regeneration, animal behavior, and soil nutrient fluxes. Measurements were made across space and time within three 1-ha plots comprising a gradient from old growth to heavy selective logging. We then combined these data with nearby weather station air temperature data, as well as measurements of topography and canopy structure derived from detailed ground surveys and airborne LIDAR.<br>Sampling design<br>Study location<br>We identified three sites along a logging intensity gradient in the Malaysian state of Sabah in Borneo. Sites each contain a 1-ha plot in lowland mixed dipterocarp forest. The plots are chosen to contrast an unlogged old growth forest with a moderately logged forest and a heavily logged forest <br>Microclimate<br>To map the microclimate thermal proxy in each plot, we installed dataloggers on a semi-regular grid pattern varying in minimum distance from 1 to 14 meters. Each datalogger was a Thermochron iButton (DS1921G, Maxim), capable of logging up to 2048 temperature values between -30°C and 70°. Each datalogger was waterproofed by wrapping in plastic paraffin film (Parafilm, Bemis) and then in light yellow duct tape. Dataloggers were attached using plastic zip-ties onto PVC stakes at a height of 1-3 cm immediately above the forest floor. The tape color was chosen to approximate the albedo of vegetation/soil, and the size of the sensor package was chosen to have a similar boundary layer to many small organisms (e.g. tree seedlings, fallen branches, large insects). The sensor packages intentionally did not include a radiation shield, as the intent was not to measure air temperature. Temperatures recorded by the loggers therefore reflect a combination of conductive, convective, and radiative heat fluxes, and can be considered a rough proxy for those experienced by small organisms. <br>Each plot contains 25 20 × 20 m2 subplots, demarcated by 1 m-high PVC stakes embedded in the soil. Each subplot also contains at its center a mesh litter trap suspended on PVC stakes at 1-m height. Exact locations for all subplot corner and center stakes were determined using ground-based Field-Map software (IFER, Jílové u Prahy, Czech Republic). Spatial positions were recorded in three-dimensional space (local x, y, z-coordinates) using an Impulse 200 Standard laser rangefinder, MapStar Module II electronic compass (Laser Technology, Colorado, USA).<br>We installed a datalogger on these stakes at the corner of each subplot and the center of each subplot. We also chose at random three focal subplots in each plot for higher-resolution sampling. Within these subplots we established a cross-type design, with six additional dataloggers deployed at 1 to 5 m distance on additional PVC stakes located near each litter trap. A total of 239 dataloggers were installed.<br>Dataloggers were deployed during the end of the dry season in late 2015. Each recorded 28 days of data at 20-minute intervals. Start times were synchronized among dataloggers within plots. The exact date of deployment was 1 November for the heavily logged plot and 9 November for the moderately logged and old growth plot. Weather conditions during November-December 2015 were consistently dry and hot, so we do not anticipate any biases from the differing start dates. In nearly all cases dataloggers were recovered in their original location, except for a small number that were transported 1-2 meters down slopes. We treated data from these as though they were in their original position. A small number of dataloggers also failed due to being lost or punctured by animal bites. 90% of dataloggers (214/239) were successfully recovered and downloaded.<br>Air temperature data<br>To compare the microclimate thermal proxy to other temperature metrics, we obtained off-plot (open site) and on-plot (below canopy) weather station data. To represent off-plot data for both the moderately and heavily logged plots, a weather station was located in a cleared area at the SAFE base camp (4.724341°N, 117.601449°E), at a distance of 2.0 km from the heavily logged plot and 3.9 km from the moderately logged plot. Data were logged continuously (Datahog, Skye Instruments, UK). Measurements included air temperature (°C) and photosynthetically active radiation (W m-2). Data were available for all of the study period. To represent off-plot data for the old growth plot, another weather station was located in a cleared area at the Maliau Basin Studies Center (4.736263°N, 116.97662°E), at a distance of 1.4 km from the plot. Available data only included photosynthetically active radiation (W m-2). Data were available for approximately 25% of the study period. We predicted air temperature values at this plot for these dates by calibrating a LOESS regression model of air temperature based on time of day (seconds after midnight) and photosynthetically active radiation, calibrated with weather station data from an open clearing at the SAFE base camp (78 km distance, 184 m lower elevation). Because of the small elevation change we did not include a further lapse rate correction for temperature. The fitted model, which had a residual standard error of 0.9°C, was used to predict off-plot air temperature at the old growth plot.<br>To represent on-plot air temperature, we located air temperature sensors (HOBO, U23-002) within radiation shields at 1.5 m height in a subplot within each plot (corresponding to a focal subplot with a higher density of microclimate dataloggers: old growth, subplot 18; moderately logged, subplot 24; heavily logged, subplot 25). Temperature was measured hourly.<br>LiDAR data<br>Discrete airborne LiDAR data were acquired by NERC's Airborne Research Facility (ARF) in November of 2014 using a Leica ALS50-II LiDAR sensor flown on a Dornier 228-20 at 41 points m-2 density, with up to four returns recorded per pulse. Georeferencing of the point cloud was ensured by incorporating data from a Leica base station in the study area. LiDAR point clouds were classified into ground and non-ground points, and used to produce a 1 m resolution canopy height model by averaging the first returns. Gaps in the canopy height model were filled by averaging neighboring cells.<br>Topography<br>The ground-mapped coordinates of the subplot corners, subplot centers, and all stems were used to construct a digital elevation model (DEM) for the plot. Elevation was interpolated onto a 1 m grid using ordinary kriging with a minimum of 4 points and search radius of 30 meters. This grid was then aligned to the LIDAR-determined location and elevation of the plot corners. The DEM was then used to estimate slope (in degrees) and cosine of aspect (with higher values indicating more southerly exposures) for each location.<br>Forest structure<br>Forest structure was determined from field surveys and from airborne laser scanning. For the field survey, all trees ≥10 cm diameter at 1.3 m height were censused in each plot in 2016. Diameter was measured at 1.3 m with a tape measure, height with laser rangefinders, and x-y position of each stem were determined using the same system as the subplot corners. The horizontal crown projection of every tree was mapped by measuring spatial positions (x and y-coordinates) of 5 to 30 points (depending on the size of the crown) at the boundary of a crown projected to the horizontal plane and then smoothed using Field-Map software. <br>Field stem maps were then converted into raster grids of stem basal area density (smoothed with 2-meter Gaussian kernel, and then rasterized to 1 m resolution), canopy density (number of overlying canopies per unit area) (1 m resolution), and plant area index (PAI) (10 m native resolution, interpolated to 1 m resolution). Spatial variation in PAI was mapped from the LiDAR point cloud using the MacArthur-Horn method. The method assumes that the leaves are randomly distributed within the laterally homogeneous canopy layers, so the PAI is proportional to the logarithm of the fraction of LiDAR pulses, β, penetrating through the canopy: PAI = -1/κ ln(β), where κ is a correction factor that accounts for canopy features, such as clumping and the distribution of leaf angles. We assumed a constant value of κ=0.7. Only the first returns, representing the first interaction of each LiDAR pulse with the canopy, are considered. We employed a lower cutoff of 2 m to avoid confusing ground returns with low-lying vegetation. PAI was estimated for point locations along a 1 m regular grid using circular sampling neighborhood of 10 m. This sampling window size is used to capture a sufficient number of LiDAR returns to avoid saturation effects in the more densely vegetated parts of the plots. This approach for calculating canopy closure may be biased, as clumping of vegetation, variation in leaf angle, and canopy edges (i.e. at gaps) should lead to spatial variation in the κ coefficient. It was not possible with our data to constrain κ using hemispherical photos due to saturation effects.</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/22"><b>Drivers of microclimate variation in disturbed forests</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC independent research fellowship (Standard grant, NE/M019160/1)</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 Biodiversity Council (Research licence JKM/MBS.1000/2/2(JLD.3((126) )</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=3266822">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Blonder_Mircoclimate.xlsx, Mircoclimate_proxy.zip</p><p><b>Blonder_Mircoclimate.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Mircoclimate datalogger data</b> (described in worksheet Data_Sheet_S1)</p><p>Description: Raw temperature measurements for all microclimate dataloggers</p><p>Number of fields: 7</p><p>Number of data rows: 444416</p><p>Fields: </p><ul><li><b>Site</b>: The name of the site, corresponding to logging treatment intensity (Field type: Categorical)</li><li><b>Tag</b>: The field code for the data logger (Field type: ID)</li><li><b>Time.elapsed..s.</b>: Number of seconds elapsed since data logger began recording (Field type: Numeric)</li><li><b>Forest.floor.temperature..degC.</b>: Microclimate proxy reading (Field type: Numeric)</li><li><b>X..m.</b>: Location of the data logger in meters east in UTM zone 50N (Field type: Numeric)</li><li><b>Y..m.</b>: Location of the data logger in meters north in UTM zone 50N (Field type: Numeric)</li><li><b>Z..m.</b>: Location of the data logger in meters above sea level (Field type: Numeric)</li></ul></li><li><p><b>Weather station data</b> (described in worksheet Data_Sheet_S3)</p><p>Description: Raw off-plot and on-plot weather station air temperature data</p><p>Number of fields: 4</p><p>Number of data rows: 1397</p><p>Fields: </p><ul><li><b>Site</b>: The name of the site, corresponding to logging treatment intensity (Field type: Categorical)</li><li><b>Time.elapsed..s.</b>: Number of seconds elapsed since data logger began recording (Field type: Numeric)</li><li><b>Off.plot.weather.station.air.temperature..degC.</b>: Air temperature measurement of the off-plot weather station (Field type: Numeric)</li><li><b>On.plot.weather.station.air.temperature..degC.</b>: Air temperature measurement of the on-plot weather station (Field type: Numeric)</li></ul></li></ol><p><b>Mircoclimate_proxy.zip</b></p><p>Description: S2 Spatial datasets for microclimate, topography, and canopy structure. All datasets are spatially interpolated to 1m resolution and projected into UTM coordinates, zone 50N. </p><p><b>Date range: </b>2015-11-01 to 2015-12-30</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Dataset supporting publication: "Data collected by coupling fix and wearable sensors for addressing urban microclimate variability in an historical Italian city"
<p>Dataset supporting publication: “Data collected by coupling fix and wearable sensors for addressing urban microclimate variability in an historical Italian city” (publication available for download: <a href="https://zenodo.org/record/3901556">GEOFIT Zenodo</a>)</p> <p>Datasets resulting from monitoring activities of Sant'Apollinare systems and climatic parameters inside and outside the building (post-intervention monitoring).</p> <p>The article presents the data collected through an extensive research work conducted in a historic hilly town in central Italy during the period 2016-2017. Data concern two different datasets: long-term hygrothermal histories collected in two specific positions of the town object of the research, and three environmental transects collected following on foot the same designed path at three different time of the same day, i.e. during a heat wave event in summer. The short-term monitoring campaign is carried out by means of an innovative wearable weather station specifically developed by the authors and settled upon a bike helmet. Data provided within the short-term monitoring campaign are analysed by computing the apparent temperature, a direct indicator of human thermal comfort in the outdoors. All provided environmental data are geo-referenced. These data are used in order to examine the intra-urban microclimate variability. Outcomes from both long- and short-term monitoring campaigns allow to confirm the existing correlation between the urban forms and functionalities and the corresponding local microclimate conditions, also generated by anthropogenic actions. In detail, higher fractions of built surfaces are associated to generally higher temperatures as emerges by comparing the two long-term air temperature data series, i.e. temperature collected at point 1 is higher than temperature collated at point 2 for the 75% of the monitored period with an average of þ2.8 [1]C. Furthermore, gathered environmental transects demonstrate the high variability of the main environmental parameters below the Urban Canopy. Diversification of the urban thermal behaviour leads to a computed apparent temperature range in between 33.2 [1]C and 46.7 [1]C at 2 p.m. along the monitoring path. Reuse of these data may be helpful for further investigating interesting correlations among urban configuration, anthropogenic actions and microclimate variables affecting outdoor comfort. Additionally, the proposed dataset may be compared to other similar datasets collected in other urban contexts around the world. Finally, it can be compared to other monitoring methodologies such as weather stations and satellite measurements available in the location at the same time.</p>
Data for: Bridging the gap between microclimate and microrefugia: A bottom‐up approach reveals strong climatic and biological offsets
<p>In the context of global warming, a clear understanding of microrefugia, microsites enabling the survival of species populations outside their main range limits, is crucial. Several studies have identified forcing factors that are thought to favor the existence of microrefugia. However, there is a lack of evidence to conclude whether, and to what extent, the climate encountered within existing microrefugia differs from the near surrounding climate. So, we adopt a "bottom-up" approach, linking marginal disconnected populations to microclimate.</p> <p>We used the southernmost disconnected and abyssal populations of the circumboreal herbaceous plant <em>Oxalis acetosella</em> in Southern France to study whether populations matching the definition of "microrefugia" occur in particularly favorable climatic conditions compared to neighboring control plots distant about 50 m to 100 m. Temperatures were recorded in putative microrefugia and in neighboring plots for approximately 2 years to quantify their thermal offsets. Vascular plant inventories were carried out to test whether plant communities also reflect microclimatic offsets.</p> <p>We found that current microclimatic dynamics are genuinely at stake in microrefugia. Microrefugia climates are systematically colder compared to those found within 50 m to 100 m distant. This pattern was more noticeable during the summer months. Abyssal populations showed stronger offsets compared to neighboring plots than the putative microrefugia occurring at higher altitudes. Plant communities demonstrate this strong spatial climatic variability, even at such a microscale approach, as species compositions systematically differed between the two plots, with species more adapted to colder and moister conditions in microrefugia compared to the surrounding area.</p>
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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.