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Data Submission Package for Manuscript 'Moving beyond the physical impervious surface impact and urban habitat fragmentation of Alaska: Quantitative Human Footprint Inference from the first large Scale 30m high-resolution Landscape Metrics Big Data Quantification in R and the Cloud' - MS2With increased globalization, man-made climate change, and urbanization, the landscape – embedded within the Anthropocene - becomes increasingly fragmented. With habitats transitioning and getting lost, globally relevant regions considered ‘pristine', such as Alaska, are no exception. Alaska holds 60% of the U.S. National Park system’s area and is of national and international importance, considering the U.S. is one of the wealthiest nations on earth. These characteristics tie into densities and quantities of human features, e.g., roads, houses, mines, wind parks, agriculture, trails, etc., that can be summarized as ‘impervious surfaces.’ Those are physical impacts and actively affecting urban-driven landscape fragmentation. Using the remote sensing data of the National Land Cover Database (NLCD; https://www.mrlc.gov/data/nlcd-2016-land-cover-alaska ), here we attempt to create the first quantification of this physical human impact on the Alaskan landscape and its fragmentation. We quantified these impacts using the well-established landscape metrics tool ‘Fragstats’, implemented as the R package “landscapemetrics” in the desktop software and through the interface of a Linux Cloud-computing environment. This workflow allows for the first time to overcome the computational limitations of the conventional Fragstats software within a reasonably quick timeframe. Thereby, we are able to analyze a land area as large as approx. 1,517,733 km2 (state of Alaska) while maintaining a high assessment resolution of 30 meters. Based on this traditional methodology, we found that Alaska has a reported physical human impact of c. 0.067%. But when assessed, we additionally overlaid other features that were not included in the input data to highlight the overall true human impact (e.g., roads, trails, airports, governance boundaries in game management and park units, mines, etc.). We found that using remote sensing (human impact layers), Alaska’s human impact is considerably underestimated to a meaningless estimate (0.067%). The state is more seriously fragmented and affected by humans than commonly assumed. Very few areas are truly untouched and display a high patch density with corresponding low mean patch sizes throughout the study area. Instead, the true human impact is likely close to 100% throughout Alaska for several metrics. With these newly created insights, we provide the first state-wide landscape data and inference that are likely of considerable importance for land management entities in the state of Alaska, and for the U.S. National Park systems overall, especially in the changing climate. Likewise, the methodological framework presented here shows an Open Access workflow and can be used as a reference to be reproduced virtually anywhere else on the planet to assess more realistic large-scale landscape metrics. It can also be used to assess human impacts on the landscape for more sustainable landscape stewardship and mitigation in policy.
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Data Submission Package for Manuscript 'Moving beyond the physical impervious surface impact and urban habitat fragmentation of Alaska: Quantitative Human Footprint Inference from the first large Scale 30m high-resolution Landscape Metrics Big Data Quantification in R and the Cloud'2024-11-05With increased globalization, man-made climate change, and urbanization the landscape – embedded within the Pyrocene as part of the Anthropocene - becomes increasingly more fragmented, with habitats transitioning and getting lost; globally relevant regions considered ‘pristine' such as Alaska are no exception. Alaska holds 60% of the U.S. National Park system’s area and is of national and international importance, considering the U.S. is one of the wealthiest nations on earth. Roads, houses, mines, wind parks, agriculture, trails, etc. are just a few of the features humans created that can be summarized as ‘impervious surfaces’. Those are physical impacts and actively affecting urban-driven landscape fragmentation. Using the remote sensing data of the National Land Cover Database (NLCD; https://www.mrlc.gov/data/nlcd-2016-land-cover-alaska ), here we attempt to create the first quantification of this physical human impact on the Alaskan landscape and its fragmentation. We quantified these impacts using the well-established landscape metrics tool ‘Fragstats’, implemented as the R package “landscapemetrics” in the desktop software and through the interface of a Linux Cloud-computing environment. This workflow allows for the first time to overcome the computational limitations of the conventional Fragstats software within a reasonably quick timeframe. Thereby, we are able to analyze a land area as large as approx. 1,517,733 km2 (state of Alaska) while maintaining a high assessment resolution of 30 meters. Based on this traditional methodology, we found that Alaska has a reported physical human impact of c. 0.067%. But when assessed, we additionally overlaid other features that were not included in the input data to highlight the overall true human impact (including governances in game management unit boundaries, park boundaries, mines, etc.). We found that using remote sensing, Alaska’s human impact is actually considerably underestimated to a meaningless estimate and that the state is more seriously fragmented and affected by humans than commonly assumed. Very few areas are truly untouched and overall it displays a high patch density with corresponding low mean patch sizes throughout the study area. Instead, the true human impact is likely close to 100% throughout Alaska for several metrics. With these newly created insights, we provide the first state-wide landscape data and inference that are likely of considerable importance for land management entities in the state of Alaska, and for the U.S. National Park systems overall, especially in the changing climate. Likewise, the methodological framework presented here shows an Open Access workflow and can be used as a reference to be reproduced virtually anywhere else on the planet to assess more realistic large-scale landscape metrics and human impacts on the landscape in an Open GIS environment for more sustainable landscape stewardship and mitigation in policy.
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200+ Global Environmental Predictors2024-11-05This dataset contains 200+ environmental predictors used for a series of scientific publications, obtained from public sources. Curated and geo-spatially aligned by Moriz Steiner.
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Data Submission Package for Manuscript 'Model-predicting Matschie's Tree Kangaroo in Papua New Guinea'These are the GIS data used for modeling Matschie's Tree Kangaroo (Huon Tree Kangaroo) in Papua New Guinea PNG; for details please see metadata. THe manuscript is currently in revision phase.
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Metadata of six NCEAS data sets in Elith et al. 2020The publication by Elith et al. 2020 publishes data from Elith et al. 2006 and consists of six data set; metadata descriptions provided here. Data are found in OSF and as an R package; details provided in Elith et al. 2020 and/or with authors.
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Data (Appendix) for Book Chapter 43: Citizen Science Experience in Lumbini/Nepali for Sarus Cranes and Lesser Adjudants (Storks) with Regmi and Huettmann 2020 Hindu Kush Himalaya: Watersheds Downhill, SpringerThis dataset consist of an appendix of citizen science data for the Sarus Crane and Adjudant storks in Lumbini and Jagdishpur Reservoir, Nepal. It's a plain MS Excel sheet.
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Data (Appendix) for Book Chapter 33: Persistent Langur (Semnopithecus) decline in Nepal with Regmi and Huettmann 2020 Hindu Kush Himalaya: Watersheds Downhill, SpringerThis dataset consists of an appendix of a GIS map of langur sp information in Nepal. The datasets are locations, presences and absences from a value-added GBIF.org query, transect data by the authors and literature data Details are specified in the book chapter by Ale et al in Regmi and Huettmann 2020. This is the first and best compiled data for this species in Nepal and shows national declines with large conservation management implications.
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Data (Appendix) for Book Chapter 25: Museum Data holdings and Libraries in Nepal and Hindu Kush Himalaya region with Regmi and Huettmann 2020 Hindu Kush Himalaya: Watersheds Downhill, SpringerThis compiled dataset consists of a value-added analysed GBIF data set in the wider Hindu Kush-Himalaya (HKH) region. The original data source is from individual national contributors found in GBIF. Data are used here for research purposes for the wider HKH region watersheds and to show institutional spread and distribution. Some major outside museums internationally are mentioned too. The dataset consists of MS Excel sheets Methods and details are specified in the book chapter by Huettmann in Regmi and Huettmann 2020. This is the first and best compiled data for the study area and is to set a start of such views and investigations towards a better and more fair access to data, as part of a better and more democratic decision-making process.
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Data (Appendix) for Book Chapter 22: Rapid Assessment of Urban Birds and GIS models of Kathmandu and Pokhara, Nepal with Regmi and Huettmann 2020 Hindu Kush Himalaya: Watersheds Downhill, SpringerThis compiled dataset consists of a field data from rapid assessment of common birds found in urban areas of Kathmandu and Pokhara, Nepal, Hindu Kush-Himalaya (HKH) region.The dataset consists of 31 bird and animal species from a detection survey of 2 transects and photos in MS Excel sheets. It is overlaid with Open Street GIS map predictors for the study areas, and model predicted with GIS. We used the following 6 layers:waterways, natural places, shop polygons, land use, roads and highways and computed proximities for each in GIS. Methods and details are specified in the book chapter by Huettmann in Regmi and Huettmann 2020. This is the first and best compiled field and GIS data for the study area and is to set a start of such views and investigations towards a better and more fair access to data, as part of a better and more democratic decision-making process. Here an example is presented using avian species and GIS habitat layers.
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Data (Appendix) for Book Chapter 37: 'Road, Railroad and Airport data for the Hindu Kush Himalaya region' with Regmi and Huettmann 2020 Hindu Kush Himalaya: Watersheds Downhill, SpringerThis compiled dataset consists of an appendix of value-added merged GIS maps for roads, railroads and airports in the wider Hindu Kush-Himalaya (HKH) region. The original data source is from individual national DIVA-GIS files and used here for research purposes for the wider HKH region watersheds. Nations included are: Nepal, India, China, Buthan, Kazachstan, Tajikistan, Kyrgystan, Uzbekistan, Turkmenistan, Afghanistan, Iran, Laos, Myanmar, Thailand, Vietnam, Pakistan, Bangladesh and Cambodia. The dataset consists of 21zip archives of these nations also covering railways and airports. Methods and details are specified in the book chapter by Huettmann in Regmi and Huettmann 2020. This is the first and best compiled data for the study area.
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Data (Appendix) for Book Chapter 28: Sarus Crane GIS Model with Regmi and Huettmann 2020 Hindu Kush-Himalaya: Watersheds Downhill, SpringerThis dataset consist of an appendix of GIS model predictions of Sarus Cranes (GRus antigone Taxonomic Serial Number TSN: 176181) in Nepal. Details are specified in the book chapter by Karmacharya et al in G.R.Regmi and F. Huettmann 2020. This is the first model for this species and shows conservation management implications for the Terai landscape between Nepal and India.
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Rabies data for Canada and Alaska/US for GIS model predictionsThis value-added data set is part of a publication by Huettmann and Hueffer (in prep) and includes the GIS layers for rabies and predictions of Canada, assessed with Alaska locations ((taken from Huettmann et al. 2015). This project compiled the best publically available rabies data for Canada, and models them for the Northern part of the the North American continent (Alaska and Arctic Canada). The environmental data sets are in a common GIS format (ESRI and ASCII grids) and are taken from public Open Access sources. The rabies data sets are point data, as rabies was reported by the Canadian Food Inspection Agency, and processed in the lab. The dataset is 5GB in size and consists of 20 files.
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Camera Trapping Grid Data in Nepal Hindu Kush-Himalaya for regions of Humla 2015 , Manang 2014-2015 and Manang 2016-2017This multi-year, multi-site and multi-species dataset describes Bushnell Camera Trap data from three locations in remote Nepal: Humla 2015, Manang 2014-2015 and Manang 2016-2017. The data from the Hindu Kush-Himalaya region comes from a geo-referenced survey grid and are stored in three MS Excel sheets, also combined and available in CSV consisting of 175658 records and 15 columns with a file size of 19MB. Species covered are Blue Sheep/Bharal (Pseudois nayaur Taxonomic Serial Number TSN 180596 ), Snow Leopard (Panthera unica, Uncia uncia TSN 183811), Beech Marten (Martes foina TSN 621941), Wolf (Canis lupus TSN 180596), birds (Aves Golden Eagle etc.),Pika (Ochotona), Red Fox (Vulpes vulpes TSN 180604), Mountain Weasel (Mustela altaica TSN 621947), Pallas�s Cat (Otocolobus manul, Felis manul TSN 183791), and Golden Jackal (Canis aureus TSN 183817)
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Red squirrel midden model prediction GIS dataThis dataset features the best-available compilation about Red Squirrels (Tamiasciurus hudsonicus, taxonomic serial number 180168 ) GIS model predictions in a study area in Fairbanks,Alaska. This dataset starts in 20016 and ends in 2017. The data are referenced in time and in space (GPS) and it consist of GIS layers for the UAF campus trails, including LIDAR; the geographic projection is UTM 6N in meters. The dara are compiled from sightings and records by the first author. This dataset represents opportunistic as well as complete sightings for a study area at UAF campus. The actual squirrel data are compiled into an MS Excel sheet and all other data layers are in ESRI format: raster or shapefile Tthe size of the overall data package is app. 21 MB.
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Distribution and density of the American Red Squirrel (Tamiasciurus hudsonicus ) in the interior Alaskan old-growth forest for 2019The aim of this project -carried out in July 2019 - was to determine the distribution and density of the American Red Squirrel (Tamiasciurus hudsonicus; taxonomic serial number 180166) in the old-growth forest of interior Alaska; region of Fairbanks. Also the distribution and density of squirrel middens (construction built by the squirrel, which is used for nutrition storing for the winter. Middens also provide as a nest for the squirrel's which can be used as protection from predators. We carried out opportunistic surveys along trails and within forest stands using GPS and notebook. Google Maps were used for navigation and planning help. This work can be used for subsequent model predictions with GIS software and other modelling software programs to obtain the detection rates and the distribution and density of middens in the whole study area (Tanana State Forest).
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Climate (temperature & humidity) data logger data from EasyLogUSB in interior AlaskaThese data are daily climate data (temperature and humidity) collected on transects in interior Alaska throughout the year. Data were collected for the years 2015, 2016 and 2017 by ski, with dogs, by bike and by car. An attached EasyLogUSB data logger was used and usually 10 second interval records were collected during a 1hour data session, or more (daily profiles, some are stationary for 24hours). Temperature is collected as degrees Celsius and humidity as percent; data exist as a txt/ASCII format in columns. These data are referenced to time and locations, and they can be used as cross-profiles for landscape climate and ground-truthing of climate models using GIS and geo-referencing. Data include the �sampling of altitudinal profiles, landscape cover, river crossings and various topographies, including coastal-interior gradients. Data collection is still ongoing.
