Feature Service generated from running Find Hot Spots

We modeled historical and future stream fish distributions using a suite of environmental covariates derived from high-resolution hydrologic and climatic modeling of the basin. We quantified variation in outcomes for individual species across climate scenarios and across space, and identified hotspots of species loss by summing changes in probability of occurrence across species. Under all climate scenarios, we find that the distribution of most fish species in the Red River Basin will contract by 2050. However, the variability across climate scenarios was more than 10 times higher for some species than for others. Despite this uncertainty in outcomes for individual species, hotspots of species loss tended to occur in the same portions of the basin across all climate scenarios. We also find that the most common species are projected to experience the greatest range contractions, underscoring the need for directing conservation resources towards both common and rare species. Our results suggest that while it may be difficult to predict which species will be most impacted by climate change, it may nevertheless be possible to identify spatial priorities for climate mitigation actions that are robust to future climate uncertainty. These findings are likely to be generalizable to other ecosystems around the world where future climate conditions follow prevailing historical patterns of key environmental covariates.

With the ongoing crisis of biodiversity loss and limited resources for conservation, the concept of biodiversity hotspots has been useful in determining conservation priority areas. However, there has been limited research into how temporal variability in biodiversity may influence conservation area prioritization. To address this information gap, we present an approach to evaluate the temporal consistency of biodiversity hotspots in large marine ecosystems. Using a large scale, public monitoring dataset collected over an eight year period off the US Pacific Coast, we developed a methodological approach for avoiding biases associated with hotspot delineation. We aggregated benthic fish species data from research trawls and calculated mean hotspot thresholds for fish species richness and Shannon’s diversity indices over the eight year dataset. We used a spatial frequency distribution method to assign hotspot designations to the grid cells annually. We found no areas containing consistently high biodiversity through the entire study period based on the mean thresholds, and no grid cell was designated as a hotspot for greater than 50% of the time-series. To test if our approach was sensitive to sampling effort and the geographic extent of the survey, we followed a similar routine for the northern region of the survey area. Our finding of low consistency in benthic fish biodiversity hotspots over time was upheld, regardless of biodiversity metric used, whether thresholds were calculated per year or across all years, or the spatial extent for which we calculated thresholds and identified hotspots. Our results suggest that static measures of benthic fish biodiversity off the US West Coast are insufficient for identification of hotspots and that long-term data are required to appropriately identify patterns of high temporal variability in biodiversity for these highly mobile taxa. Given that ecological communities are responding to a changing climate and other environmental perturbations, our work highlights the need for scientists and conservation managers to consider both spatial and temporal dynamics when designating biodiversity hotspots.

Incorporating interactions into a biogeographical framework may serve to understand how such interactions and the services they provide are distributed in space. We begin by simulating the spatiotemporal dynamics of realistic mutualistic networks inhabiting spatial networks of habitat patches. We proceed by comparing these predictions with the empirical results of a set of pollination networks in isolated hills of the Argentinian Pampas. We first find that one needs to sample up to five times as much area to record interactions as would be needed to sample the same proportion of species. Second, we find that peripheral patches have fewer interactions and harbor less nested networks ---therefore potentially less resilient communities--- compared to central patches. Our results highlight the important role played by the structure of dispersal routes on the spatial distribution of community patterns. This may help to understand the formation of biodiversity hotspots.

Feature Service generated from running the Find Hot Spots solution.

The approach employs a detailed desk study using digital data within a geographic information system (GIS) to identify Integrates Habitat Networks (IHNs). The spatial position and extent of functional integrated habitat networks were determined through a landscape ecology model from the BEETLE (Biological and Environmental Evaluation Tools for Landscape Ecology) suite of tools.

The following report outlines the workflow used to optimize your Find Hot Spots result:There were 796 valid input features.WHITE_CY Properties:Min0.0000Max6.7370Mean0.1997Std. Dev.0.3187There were 8 outlier locations; these were not used to compute the optimal fixed distance band.Scale of AnalysisThe optimal fixed distance band was based on the average distance to 30 nearest neighbors: 2734.0000 Meters.Hot Spot AnalysisThere are 470 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.OutputRed output features represent hot spots where high WHITE_CY values cluster.Blue output features represent cold spots where low WHITE_CY values cluster.

The following report outlines the workflow used to optimize your Find Hot Spots result:Initial Data Assessment.There were 2847 valid input features.There were 21 outlier locations; these were not used to compute the polygon cell size.Incident AggregationThe polygon cell size was 332.0000 Meters.The aggregation process resulted in 865 weighted areas.Incident Count Properties:Min1.0000Max216.0000Mean3.2913Std. Dev.10.3688Scale of AnalysisThe optimal fixed distance band was based on the average distance to 30 nearest neighbors: 1652.0000 Meters.Hot Spot AnalysisThere are 116 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.OutputRed output features represent hot spots where high incident counts cluster.Blue output features represent cold spots where low incident counts cluster.

This is an interactive map showing potential fire locations identified on satellite imagery from different datasets across Puerto Rico and U.S. Virgin Islands for the year 2023. The dataset was download from Fire Information for Resource Management System (FIRMS). You can find current and past fire/hotspot detection in the previous link.

The following report outlines the workflow used to optimize your Find Hot Spots result:There were 417 valid input features.PPPOINT_ID Properties:Min33.0000Max10999.0000Mean1273.2566Std. Dev.2397.9936There were 10 outlier locations; these were not used to compute the optimal fixed distance band.Scale of AnalysisThe optimal fixed distance band selected was based on peak clustering found at 95089.8395 Meters.Hot Spot AnalysisThere are 34 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.OutputRed output features represent hot spots where high PPPOINT_ID value cluster.Blue output features represent cold spots where low PPPOINT_ID value cluster.

The following report outlines the workflow used to optimize your Find Hot Spots result:There were 2941 valid input features.CONFIRMED Properties:Min1.0000Max207353.0000Mean654.5179Std. Dev.4780.3975There were 64 outlier locations; these will not be used to compute the optimal fixed distance band.Scale of AnalysisThe Neighborhood Distance used was 50 Miles.Hot Spot AnalysisThere are 70 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.OutputRed output features represent hot spots where high CONFIRMED value cluster.Blue output features represent cold spots where low CONFIRMED value cluster.

The Executive Yuan and its affiliated agencies at all levels (organs, and local governments around the country) have set up over 9,000 WiFi hotspots in selected public areas to provide free wireless internet basic information services for the public to improve service quality and meet the networking needs of people going out or waiting for official business. This service provides a condition inquiry function, and the public can use four filtering conditions (providing agencies, keywords, counties and cities, and place types) to query hotspots and download hotspot query results (competent authorities, regions, hotspot names, addresses, latitudes and longitudes) to find free WiFi hotspots in the public areas of iTaiwan.

Open science is vital to the interdisciplinary field of ecology due to its integrative nature and use of longitudinal datasets that build upon earlier data collections. To highlight the importance of open science in the rapidly growing discipline of restoration ecology, we conducted a 'computational reproducibility' assessment of a publication on a mining restoration program spanning several decades and over 250 km² in a global biodiversity hotspot. Open data and code provided alongside the original publication were assessed for consistency with the results and conclusions of the original publication, as were potential limitations in findings due to the methodology. The impacts of inconsistencies and limitations were qualitatively assessed against the key findings from the publication and data were re-analysed where impacts were potentially significant. Of the six inconsistencies and limitations identified, two had a significant impact on five of the 11 key findings of the original publication, and one new key finding was made. The impact of this is of high ecological significance as the findings related to key restoration parameters: species richness (similarity of species richness between forest and 25-year-old restoration), functional diversity (correlation of species richness and functional diversity), and the restoration trajectory (long term trends and restored areas' resilience to disturbance). These outcomes highlight the importance of open data and the value of detailed third-party data reviews, particularly in restoration ecology which relies on research findings to inform decision-making and policy and drive adaptive management.

Tourist congestion at hot spots has been a major management concern for UNESCO World Heritage Sites and other iconic protected areas. A growing number of heritage sites employ technologies, such as cameras and electronic ticket-checking systems, to monitor user levels, but data collected by these monitoring technologies are often under-utilized. In this study, we illustrated how to integrate data from hot spots by camera-captured monitoring and entrance counts to manage use levels at a World Heritage Site in Southeastern China. 6,930 photos of a congestion hotspot (scenic outlook on a trail) were collected within the park at a 10-minute interval over 105 days from January to November 2017. The entrance counts were used to predict daily average and maximum use level at the hotspots. Results showed that the average use level at the congestion hotspot did not exceed the use limit mandated by the park administration agency. However, from 9:20 am to 12:00 pm, the use level at hotspots exceeded visitor preferred use level. Visitor use level was significantly higher at the hotspot during a major Chinese “Golden Week”. The daily entrance counts significantly predicted the average and maximum use level at the hotspot. Based on our findings, park managers can achieve the management goals by permitting the corresponding number of visitors passing the entrances. The gap manifested the complexities in visitor capacity management at high-use World Heritage Sites and other protected areas and calls for innovative monitoring and management strategies.

The following report outlines the workflow used to optimize your Find Hot Spots result:There were 866 valid input features.WHITE Properties:Min0.0000Max9979.0000Mean1405.8406Std. Dev.1754.9340There were 10 outlier locations; these were not used to compute the optimal fixed distance band.Scale of AnalysisThe optimal fixed distance band was based on the average distance to 30 nearest neighbors: 2551.0000 Meters.Hot Spot AnalysisThere are 700 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.OutputRed output features represent hot spots where high WHITE values cluster.Blue output features represent cold spots where low WHITE values cluster.

This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source: NASA FIRMS - Active Fire Data - for WorldScale/Resolution: 1kmUpdate Frequency: 1/2 Hour (every 30 minutes) using the Aggregated Live Feed MethodologyArea Covered: WorldWhat can I do with this layer?The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Additional InformationMODIS stands for MODerate resolution Imaging Spectroradiometer. The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. The orbit of the Terra satellite goes from north to south across the equator in the morning and Aqua passes south to north over the equator in the afternoon resulting in global coverage every 1 to 2 days. The EOS satellites have a ±55 degree scanning pattern and orbit at 705 km with a 2,330 km swath width.It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.Attribute InformationLatitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.Acquisition Date: Derived Date/Time field combining Date and Time attributes.Satellite: Whether the detection was picked up by the Terra or Aqua satellite.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.RevisionsJune 22, 2022: Added 'HOURS_OLD' field to enhance Filtering data. Added 'Last 7 days' Layer to extend data to match time range of VIIRS offering. Added Field level descriptions.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Meeting future food demand will require transformations toward sustainable and resilient food systems that simultaneously increase production, minimize environmental impacts, and adapt to climate change. With fluctuations in temperature and precipitation exercising a growing influence on production stability across the planet, a detailed understanding of where cropping patterns are vulnerable to climatic stresses is a missing yet critical step for developing solutions that enhance the climate resilience of crop production. Here we address this urgent need by combining gridded climate data, spatially-explicit agricultural statistics, and process-based crop modeling to quantify global patterns of rainfed and irrigated crop climate sensitivity (measured as the percent reduction in median yield under extreme climate conditions) and climate-associated production losses for 17 major crops, accounting for 75% of global primary production. This climate sensitivity metric is ideally suited for identifying locations where each crop tends to be subject to high climate variability and where crop production may be susceptible to high climate-related production losses. We estimate -10.1% and -6.8% losses in global rainfed and irrigated production (respectively) under historically observed extreme climate conditions - enough calories to feed 2.1 billion people - and find hotspots of climate sensitivity in the central US, eastern Brazil, the Mediterranean basin, and South Asia, among other regions. We then focus on monsoon cereals (rice, maize, millet, sorghum) to illustrate how sustainable irrigation expansion and targeted crop switching could reduce climate sensitivity, finding that 62% of production losses could be avoided while increasing overall production by 14%. Our new scalable and universal approach to measuring the climate sensitivity of crops enables the assessment of where climate-related production losses tend to be largest and where mitigating actions and investments can be proactively targeted to better ensure the stability and increased supply of global crop production.

Background: Recurrent pregnancy loss (RPL) is a suffering pregnancy disease defined by the spontaneous loss of two or more pregnancies. The etiology of RPL is complex, and its pathogenesis has not been elucidated. Therefore, it’s necessary to combine relevant articles to find and analyze the contacts between the research hotspots, and then predict the development trend in RPL.

Methods: RPL related literature was obtained from the Web of Science (WoS) database from 2014 to 2024. We analyzed the data by VOSViewer for the number of publications, citations, countries, journals, institutions, authors and documents, and for data sorting and visualization and Citespace 6.2 R6 software was used for clustering and emergent analysis of keywords.

The social and spatial organisation of avian societies is often complex and dynamic with individuals socialising with others in a local population. Although social interactions can readily be described in colonial breeders through the location of nests, social interactions regularly take place in other contexts that are often not considered. Social behaviour in the colonially breeding zebra finch, Taeniopygia guttata, has been the focus of much work in the laboratory, but very little is known about their social organisation in free-living populations, especially outside the breeding context. Here we characterise semi-permanent gathering locations, or ‘social hotspots’ in the zebra finch in the wild. We determined the use of such social hotspots and the resulting group dynamics by quantifying movements to and from these locations through direct observation and by quantifying the vocal activity at these locations using acoustic recorders. We show that, throughout the day, zebra finches regularly visit these hotspots, and the hotspots are occupied for a substantial proportion of the day. Individuals typically arrived and left in pairs, or small groups, indicating that these social hotspots do not function just for flock formation. Instead, the high levels of vocal activity at these hotspots indicate that they may potentially function as local hubs for socialisation and information exchange, whilst also perhaps providing safety-in-numbers benefits to individuals during periods of resting. These findings characterise an important component of the natural social life of one of the most widely studied birds in captivity. The characterisation of these social hotspots highlights the use of landmarks by birds to facilitate social contacts, cohesion, and behaviour, in a social bird. Similar hangouts and social hotspots may be a feature of social behaviour in other multi-level aggregative species in which the fission and fusion of groups is an important component of daily life. Methods This dataset collection describes data from three different methods that we combined in order to describe the presence and usage of social hotspots in wild zebra finches. These hotspots are temporally stable locations, either in bushes or (shrub-like) trees, where zebra finches come and go, and spend substantial time. Hangouts, periods of time when the hotspot is occupied, regularly take place at these meeting locations. The data was collected from October–December 2019 at UNSW Fowlers Gap Arid Zone Research Station in New South Wales, Australia, home to a well-studied population of wild zebra finches. The three methods are:a) Dropping countsWe counted zebra finch droppings under bushes/trees in 10x10 cm squares in order to identify and/or confirm which vegetation was substantially used by zebra finches. For each site we identified also a 'control tree' which was nearby and similar, but where we observed a lower amount of droppings. These 'social tree' – 'control tree' pairs were then used for the rest of the study. The droppings.csv dataset contains repeated (over several days) dropping counts for these pairs (n = 10 tree pairs).b) Focal observationsWe conducted two full-day observations (from before dawn till dusk) of social hotspot and control tree pairs (n = 7 tree pairs), where we quantified the group size of each arriving and departing group of zebra finches, as well as kept track of the number of zebra finches on the social hotspot and the control tree. This dataset describes the group dynamics at these sites over time. The hangout_observations.csv dataset contains all the arrival and departure events that we observed during these observation days.c) Audio recordingsWe put time-programmed recorders in the social hotspots and control trees (n = 10 tree pairs) to quantify how much time zebra finches were spending at each respective tree, and more precisely, how often vocal activity took place. To do this, we measured for one full day of recording (about 12h) every bout of zebra finch vocal activity (where a silence of 5 minutes demarcates the end of a vocal activity bout). Several of these recorded days were the same as the days during which we did the focal observations, so that we could also compare and validate the results of both methodologies. The hangout_acoustics.csv dataset contains all the vocal activity bouts.For further details on how the data was collected, we refer to the (open-access) related article in Journal of Avian Biology.

Marine debris poses significant threats to coastal ecosystems and infrastructure, especially in semi-enclosed regions where monitoring is limited by inaccessibility and uneven population distribution. In Howe Sound, British Columbia, this study integrates remote sensing and environmental modeling to predict debris accumulation zones. Sentinel-2 satellite imagery was selected due to its high spatial resolution, broad spectral range, and frequent revisit time, which make it well-suited for capturing detailed coastal features. A neural network algorithm was used to classify six landcover types with an overall accuracy of 0.98. The classification results showed that many known debris hotspots are located near urban shorelines and within semi-enclosed bays. To simulate debris transport, river discharge and seasonal wind direction were modeled as surface movement drivers. The study area was divided into three sections to account for spatial variation in debris driving forces contribution. Hourly wind data from four weather stations were used to construct wind rose diagrams that captured seasonal changes in wind direction. The simulation identified 49 predicted debris hotspot locations. Of these, 20 overlapped with known hotspots, while 10 of the 29 newly identified hotspots are in less populated and previously underreported areas, particularly along the western shoreline. These findings demonstrate that remote sensing, when combined with physically based modeling, can overcome limitations of traditional monitoring methods and improve the identification of marine debris accumulation. This approach provides a scalable and transferable framework for supporting more targeted and proactive coastal management strategies.