Conversational Marketing mit dem Kommunikationsinstrument WhatsApp: Analyse von Potentialen und Herausforderungen für österreichische Unternehmen

eingereicht von Moriz Steiner, BAerstellt am Fachhochschul-Studiengang Digital Business Management FH OÖ, Standort SteyrMasterarbeit Johannes Kepler Universität Linz 2024Arbeit auf den öffentlichen PCs in den Bibliotheken der JKU+Medizin abrufba

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.Ye

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' - MS2

With 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.Ye

Where Do the World’s Squirrel Hotspots and Coldspots of 230+ Species Go with Climate Change 2100? A First BIG DATA Minimum Estimate from an Open Access Climate Niche Rapid Model Assessment

Abstract Man-made climate change and its impact on the living world remain the problem of our time waiting for a good science-based resolution. Here, we focus on forecasting the global squirrel population as a representative but overlooked species group for the year 2100. This was possible by using 230 publicly available Species Distribution Model prediction maps for the world’s squirrels (233 out of 307; 75%). These distribution forecasts are originating from 132 GIS predictors, implemented with an ensemble of three machine learning algorithms (TreeNet, RandomForest, and Maxent). We found that most of the world’s squirrel ranges will be shifting (usually towards higher altitudes and latitudes) and remain/ become more fragmented; some species extend their range, and many can ‘spill’ into new landscapes. Considering that here we just ran a Rapid Assessment of Big Data, dealing with a climate niche envelope of the future but not the entire more holistic perspective of climate change and 2100, we assume wider serious changes will occur for squirrels, their habitats, and the world in the future Anthropocene of 2100. These changes can lead to more stress, genetic loss, extinction, and increased zoonotic disease transmissions, and this process will occur with an increased gradient over time.</jats:p

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.Ye

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

Abstract Background Describing where distribution hotspots and coldspots are located 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 described primate species and the best-available predictor set. These Super SDMs are conducted using an ensemble of modern Machine Learning algorithms, including 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 Open Access environmental predictor set of 201 layers. For this analysis, all occurrences have been merged into one multi-species (400+ species) pixel-based analysis. Results We present the first quantified pixel-based global primate hotspot prediction 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 non-human primates occur (or not). Conclusions 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 reasoning. Those areas should be considered of the highest conservation management priority, ideally following ‘no killing zones’ and sustainable land stewardship approaches if primates are to have a chance of survival

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'2

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.Ye

Distribution and density of the American Red Squirrel (Tamiasciurus hudsonicus ) in the interior Alaskan old-growth forest for 2019

The 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)

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'_2

With 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.Ye