Huettmann, Falk
Recent Submissions
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Data Submission Package for Manuscript 'Alaska as a failed socio and ecological state? Some supporting evidence from the absence of carnivorous plant trait conservation using open access and ensemble model predictions'Plants with carnivorous traits are of great importance. However, it is globally noticed that without effective conservation, a declining number of suitable habitats, and the wide destruction of currently realized niches, their future does not appear bright. In a region that had all possibilities to “get things right” upon its statehood being granted in 1959, Alaska has, up to the present moment, consistently fallen short in stabilizing an effective conservation system for this group of plant traits and its habitats. Coupled with many other socio-ecological issues that persist from the colonial and industrial past and worsen over time, as well as the rise of new issues such as global climate change and “the great acceleration,” Alaska presents virtually all the typical indicators of a failed socio-ecological state (as a term used by The World Bank). For a wider assessment, here we compiled all Open Access data in the public realm related to eight carnivorous plant species in Alaska and overlaid them for an assessment with nine predictor layers. Beyond raw data, we developed ensemble models serving the purpose of indicating generalized hotspots and coldspots of plants with carnivorous traits, also using citizen-science occurrence data. Further, we analyzed the predicted occurrence with the underlaying land ownership/use types as well as mining claims as an example of a leading industrial activity. Alaska actually hosts the majority of the US National Park System, but we find the majority of carnivorous plants located outside, and a higher predicted occurrence within officially designated mining areas than outside. In our assessment, we see no relevant policy, vision, efficient action, or principles of strategic conservation management applied to plants and their traits, specifically carnivorous plants in Alaska and its leadership. Judged by the major socio-ecological metrics, it confirms evidence that Alaska does not present basic performance metrics of good natural resource management, and thus it would meet the definition of a failed state.
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Data Submission Package for Manuscript 'Using Machine Learning, the Cloud, Big Data, Citizen-science, and the world’s largest set of environmental predictors towards proposing modern add-ons to improve conservation management plans for squirrel species in Alaska and Indigenous lands'Context. Squirrel species in Alaska generally lack thorough conservation management plans, and they are actively hunted with no bag limits, closed seasons, or any other restrictions. This indicates a laissez-faire approach to Alaskan squirrel conservation management. Aims. In an attempt to improve this current situation, we employ an ensemble of machine-learning algorithms as proposed improvement add-ons to the traditional components of conservation management plans toward a more state-of-the-art approach to squirrel conservation. Methods. We combined the Machine Learning algorithms TreeNet, CART, Random Forest, and Maxent with over 200 environmental and socio-economic predictors for the ensemble Super Species Distribution Models. These ensemble models were carried out for all squirrel species individually occurring in Alaska and a 600 km buffer area and two assemblage models combined: a) all species currently occurring only in Alaska and b) all species occurring in Alaska and the 600km buffer area. Key results. Most predicted distribution hotspots for squirrels in Alaska and the 600 km buffer area were observed near road and river systems (close to human activities) and the last glacial maximum refugia. Conclusions & Implications. Applying a machine learning ensemble distribution modeling framework to conservation management plans can add valuable science-based insights to better understand the landscape and species to be managed. This can also be highly valuable for lands not directly managed by conventional agencies, e.g., land managed by the military or Native communities.
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Data Submission Package for Manuscript 'Progress on the world's primate hotspots and coldspots: Modeling ensemble Super SDMs in cloud-computers based on digital citizen-science Big Data and 200+ predictors for more sustainable conservation planning'2Describing where distribution hotspots and coldspots are located with certainty is crucial for any science-based species management and governance. Thus, here we created the world’s first Super Species Distribution Models (SDMs) including all primate species and the best-available predictor set. These Super SDMs are conducted using modern Machine Learning ensembles like Maxent, TreeNet, RandomForest, CART, CART Boosting and Bagging, and MARS with the utilization of cloud supercomputers (as an add-on option for more powerful models). For the global cold/ hotspot models, we obtained global distribution data from www.GBIF.org (approx. 420,000 raw occurrence records) and utilized the world’s largest environmental predictor set of 201 layers. For this analysis, all occurrences have been merged into one multi-species (400+ species) pixel-based analysis. We quantified the global primate hotspots for Central and Northern South America, West Africa, East Africa, Southeast Asia, Central Asia, and Southern Africa. The global primate coldspots are Antarctica, the Arctic, most temperate regions, and Oceania past the Wallace line. We additionally described all these modeled hotspots/coldspots and discussed reasons for a quantified understanding of where the world’s primates occur (or not). This shows us where the focus for most future research and conservation management efforts should be, using state-of-the-art digital data indication tools with reason. Those areas should be considered of the highest conservation priority, ideally following ‘no killing zones’ and sustainable land stewardship approaches if primates are to have a chance of survival.
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Data Submission Package for Manuscript 'Progress on the world's primate hotspots and coldspots: Modeling ensemble Super SDMs in cloud-computers based on digital citizen-science Big Data and 200+ predictors for more sustainable conservation planning'Describing where distribution hotspots and coldspots are located with certainty is crucial for any science-based species management and governance. Thus, here we created the world’s first Super Species Distribution Models (SDMs) including all primate species and the best-available predictor set. These Super SDMs are conducted using modern Machine Learning ensembles like Maxent, TreeNet, RandomForest, CART, CART Boosting and Bagging, and MARS with the utilization of cloud supercomputers (as an add-on option for more powerful models). For the global cold/ hotspot models, we obtained global distribution data from www.GBIF.org (approx. 420,000 raw occurrence records) and utilized the world’s largest environmental predictor set of 201 layers. For this analysis, all occurrences have been merged into one multi-species (400+ species) pixel-based analysis. We quantified the global primate hotspots for Central and Northern South America, West Africa, East Africa, Southeast Asia, Central Asia, and Southern Africa. The global primate coldspots are Antarctica, the Arctic, most temperate regions, and Oceania past the Wallace line. We additionally described all these modeled hotspots/coldspots and discussed reasons for a quantified understanding of where the world’s primates occur (or not). This shows us where the focus for most future research and conservation management efforts should be, using state-of-the-art digital data indication tools with reason. Those areas should be considered of the highest conservation priority, ideally following ‘no killing zones’ and sustainable land stewardship approaches if primates are to have a chance of survival.
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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'_2With 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' - 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.