Wildlife is often poorly managed, poaching is part of such a problem. In times of globalization, ... more Wildlife is often poorly managed, poaching is part of such a problem. In times of globalization, international demands can easily put huge pressures on endemic species in remote locations and small nations. Nepal faces such a situation and here we present the first study of wildlife poaching, as identified from media reports. Many species were identified from confiscation reports made by local police and border guards. We focused on the Nepali-Chinese and Nepali-Indian border and present numbers, estimates and an outlook on what to do regarding poaching reduction.
This data set contains the morphometric information for birds caught in mistnets and with other m... more This data set contains the morphometric information for birds caught in mistnets and with other means for sampling Avian Influenza (AI). These data were collected during fall migration in the Hokkaido region, Japan, at 11 locations (Daikoku-tou Muroran-City latitude 42.2082 longitude 140.5567, Erimo-cho latitude 40.0000 longitude 143.1000, Lake Komuke Monbetsu latitude 44.1600 longitude 143.3000, Lake Utonai Tomakomai latitude 42.4237 longitude 141.4262, Naganuma-cho latitude 42.5437 longitude 141.4014, Rubeshibe latitude 43.4700 longitude 143.3400 Shiragami Matsumae-cho latitude 41.2400 longitude 140.1100 Shunkunitai Nemuro latitude 43.1590 longitude 145.2753 WLC (patient) latitude 43.0400 longitude 144.1700 WLC Kushiro latitude 43.0400 longitude 144.1700 WLC(patient) latitude 43.0400 longitude 144.1700 ) during fall (15th June - 10th October 2006) in collaboration with the University of Alaska-Fairbanks, USA. It was assumed that some birds in that region link with Alaska. This dataset focuses on passerines, raptors, shorebirds and seabirds. Avian Influenza samples were taken from from 74 species and available morphometric measurement are reported for theme in the dataset. Avian Influenza analysis of samples is coming forward, and will be published elsewhere. Here we present on traditional bird banding and opportunistic measurements of birds caught in the framework of the AI sampling campaign 2006. This dataset links with 6 other sites in the Russian Far East, and is compatible to other bird banding studies along the flyway. It represents for the first time a coordinated study done during fall migration and with identical methods at 6 sites within the Sea of Okhotsk region, Russian Far East. Data from the other study sites are also available online. An analsis of these data is coming forward; for details please contact authors and project collaborators.
The application of machine learning algorithms in ecology has surged forward in the past two deca... more The application of machine learning algorithms in ecology has surged forward in the past two decades. More and more, we are seeing innovative and interesting uses of these sophisticated algorithms which are driving inference and understanding in natural resource management. The concept behind machine learning is to provide data to a computer and allow the machine to ‘learn’ the patterns in those data. These learned relationships are applied and analysed in a variety of ways from clustering to prediction. In ecology and natural resource management, these methods are not well taught in classroom settings, which is leading to a major disconnect between ecologists and the latest analytical techniques. In this chapter, we introduce machine learning with a focus on ecological and natural resource management applications. We provide definitions and a list of a few key algorithms that are becoming commonplace in the analysis of wildlife data. We further introduce a few broad concepts (i.e., data sharing, metadata and citizen science) in the sphere of ecological sciences. The ideas presented here will help us to better understand how to apply machine learning for conservation, management, and academic studies. These examples can also be used to teach the next generation of scientists how to best use machine learning algorithms in their own work. Our broader goals here, and elsewhere in this book, is to promote a holistic understanding of our planet through algorithms that can handle many interacting covariates that represent how the world really works.
The future climate models show dramatic decay of the status quo (see Xenarios et al. 2019 for Cen... more The future climate models show dramatic decay of the status quo (see Xenarios et al. 2019 for Central Asia and its mountain regions). This is not only true in all relevant weather metrics (e.g. http://worldclim.org/CMIP5v1) but also in terms of glacier loss (https://www.theguardian.com/environment/2019/jun/19/himalayan-glacier-melting-doubled-since-2000-scientists-reveal), habitat transition and wilderness loss (Huettmann 2017), as well as water problems (Karar 2017; Singh et al. 2018; World Water Council 2018; Craymer 2019) which reach far outside of localized problems promoting conflicts and wars elsewhere. And it links directly with many human-related statistics such as population rise, consumption and loss of language, society and culture (Xu et al. 2018 for the Hindu-Kush Himalaya region; see Hinze 2019 for remote monastries shutting down, lack of young people and missing lifestyle). The classic concept of ‘Sustainable Development’ is not sustainable at all and a new governance scheme is to be found serving humans and the earth better.
The Pallas’s cat (Otoclobus manul) has recently been discovered in the Manang valley of Annapurna... more The Pallas’s cat (Otoclobus manul) has recently been discovered in the Manang valley of Annapurna Conservation Area, Nepal by a citizen scientist and a field biologist of Third Pole Conservancy Mr. Tashi R. Ghale. With camera-trapped images and footage evidence, it has been established that the valley is also inhabited by other predators namely snow leopard (Panthera uncia), Grey wolf (Canis lupus), Golden jackal (Canis aureus), Red fox (Vulpes vulpes), Beech marten (Martin foina) and Mountain weasel (Mustella altaica) besides the Pallas’s cat. The Pallas’s cat has been living in the narrow range within a specialized habitat of south-facing sloped rugged terrain of the Manang valley where the Pikas (Ochotona spp.) are abundant. The major survival threats of this ancient, primitive and elusive small cat in the valley are: habitat degradation, prey-base decline and perhaps diseases and the human-induced climate change which directly affects its sensitive and fragile habitat. Future conservation research should focus on accurate density and population estimation of this cat and its principal prey-base i.e. Pika; and how its habitat-complex has been changing over time. The research on trophic cascades would help to understand how the Pallas’s cat is distributed over time and space as part of the wider carnivore community in the Manang valley.
Massive strides have been made in the fields of machine learning and data mining with new algorit... more Massive strides have been made in the fields of machine learning and data mining with new algorithms developing at incredible rates. These advances have come along in the past two decades and have vastly changed almost every aspect of our lives. Thus, it is nearly impossible to envision what these algorithms will be like by the year 2100 (a benchmark year for climate and therefore, ecological forecasting). New synergies between technology and the environment are bound to occur, and in this chapter we discuss some of the ways that could happen, while visiting some of the advancements in machine learning to date. We put this in the context of ecology and natural resource management and argue for better integration of machine learning methods into the ecological process. We also make some commentary on what machine learning tools and techniques will be critical to ecology now and into the near future. Finally, we highlight the concept of holistic ecological modeling by using more than just environmental parameters (i.e. sea surface temperature, rainfall, etc.…). We suggest an approach that includes environmental, anthropogenic, inter (and intra) species, and cosmic parameters (i.e. length of day, season, solar maxima/minima, etc.…). We believe that holistic ecosystem modeling with machine learning as a guide will greatly improve our ability to manage the natural world, when fast and accurate decisions need to be made.
Much of the Hindu Kush-Himalaya (HKH) represents a vast wilderness area that is still relatively ... more Much of the Hindu Kush-Himalaya (HKH) represents a vast wilderness area that is still relatively little affected by humans and modern industry, thus far. It matters for land, sea, atmosphere and the universe. This area consists of 18 nations; it features app. 30% of the world’s gross domestic product (GDP) and it helps to feed and to sustain the global population of this world. It further affects the world’s weather systems and climate, as well as distribution of wealth and global warfare, but also science and spiritual efforts are affected. This landscape is not just a study box of biogeography but it affects most of humanity instead! Water is an essential and connecting component in this set up, but by now it represents a heavily stressed and poorly considered and managed resource. In the HKH region eleven major rivers of global scale and relevance feed a large range and diversity of landscapes and cultures with fresh water. It is widely used for farming, drinking, irrigation, hydrodams, fishing and for estuary replenishment linking mountain peaks with oceans. Here an overview is provided for the Anthropocene across those 18 nations helping to better understand and manage more holistically this complex Hindu Kush-Himalaya area of relevance for mankind and global well-being.
African swine fever (ASF) is a viral disease, endemic to Africa, that causes high mortality when ... more African swine fever (ASF) is a viral disease, endemic to Africa, that causes high mortality when introduced into domestic pig populations. Since the emergence of p72-genotype II African swine fever virus (ASFV) in Georgia in 2007, an ASF epidemic has been spreading across Europe and many countries in Asia. The epidemic first reached Ukraine in 2012. To better understand the dynamics of spread of ASF in Ukraine, we analyzed spatial and temporal outbreak data reported in Ukraine between 2012 and mid-2023. The highest numbers of outbreaks were reported in 2017 (N = 163) and 2018 (N = 145), with overall peak numbers of ASF outbreaks reported in August (domestic pigs) and January (wild boars). While cases were reported from most of Ukraine, we found a directional spread from the eastern and northern borders towards the western and southern regions of Ukraine. Many of the early outbreaks (before 2016) were adjacent to the border, which is again true for more recent outbreaks in wild boar,...
This chapter summarizes rare information and data on how biodiversity and wildlife were governed ... more This chapter summarizes rare information and data on how biodiversity and wildlife were governed on a landscape scale during and after Rana/Royal times in Nepal. Such governance was virtually practiced since deep time, and it still carries much weight, beyond tradition. It’s probably among the longest-lasting governance schemes for watersheds in the study area of the Hindu Kush-Himalaya region. The wildlife and landscape of the lowland Terai region were historically safe and almost untouched before the Rana regime. That’s because people were scared of living in the Terai region due to the widespread presence of epidemic and fatal malaria. During the Rana regime, huge hunting parties (the infamous ‘shikaars’) by the Royal and Rana families and their foreign guests hunted hundreds of rhino, tiger and other animals in the Terai region. The status of wildlife and their habitats started improving again when the government established a protected areas network from 1973 onwards.
<p>List of modeled small mammal species scientific and common names, their associated Taxon... more <p>List of modeled small mammal species scientific and common names, their associated Taxonomic Serial Number (TSN), the number of presence and absence locations used to train models, the resultant area under the receiver operator characteristic (AUC ROC; 0–1), the % of correctly identified presences (specificity), the % of correctly identified absences (sensitivity) and overall % error across all presences and absences.</p
SummaryDeclines in populations of the Critically Endangered Spoon-billed Sandpiper Calidris pygma... more SummaryDeclines in populations of the Critically Endangered Spoon-billed Sandpiper Calidris pygmaeus have been rapid, with the breeding population now perhaps numbering fewer than 120 pairs. The reasons for this decline remain unresolved. Whilst there is evidence that hunting in wintering areas is an important factor, loss of suitable habitat on passage and wintering areas is also of concern. While some key sites for the species are already documented, many of their wintering locations are described here for the first time. Their wintering range primarily stretches from Bangladesh to China. Comprehensive surveys of potential Spoon-billed Sandpiper wintering sites from 2005 to 2013 showed a wide distribution with three key concentrations in Myanmar and Bangladesh, but also regular sites in China, Vietnam and Thailand. The identification of all important non-breeding sites remains of high priority for the conservation of the species. Here, we present the results of field surveys of wi...
Wildlife is often poorly managed, poaching is part of such a problem. In times of globalization, ... more Wildlife is often poorly managed, poaching is part of such a problem. In times of globalization, international demands can easily put huge pressures on endemic species in remote locations and small nations. Nepal faces such a situation and here we present the first study of wildlife poaching, as identified from media reports. Many species were identified from confiscation reports made by local police and border guards. We focused on the Nepali-Chinese and Nepali-Indian border and present numbers, estimates and an outlook on what to do regarding poaching reduction.
This data set contains the morphometric information for birds caught in mistnets and with other m... more This data set contains the morphometric information for birds caught in mistnets and with other means for sampling Avian Influenza (AI). These data were collected during fall migration in the Hokkaido region, Japan, at 11 locations (Daikoku-tou Muroran-City latitude 42.2082 longitude 140.5567, Erimo-cho latitude 40.0000 longitude 143.1000, Lake Komuke Monbetsu latitude 44.1600 longitude 143.3000, Lake Utonai Tomakomai latitude 42.4237 longitude 141.4262, Naganuma-cho latitude 42.5437 longitude 141.4014, Rubeshibe latitude 43.4700 longitude 143.3400 Shiragami Matsumae-cho latitude 41.2400 longitude 140.1100 Shunkunitai Nemuro latitude 43.1590 longitude 145.2753 WLC (patient) latitude 43.0400 longitude 144.1700 WLC Kushiro latitude 43.0400 longitude 144.1700 WLC(patient) latitude 43.0400 longitude 144.1700 ) during fall (15th June - 10th October 2006) in collaboration with the University of Alaska-Fairbanks, USA. It was assumed that some birds in that region link with Alaska. This dataset focuses on passerines, raptors, shorebirds and seabirds. Avian Influenza samples were taken from from 74 species and available morphometric measurement are reported for theme in the dataset. Avian Influenza analysis of samples is coming forward, and will be published elsewhere. Here we present on traditional bird banding and opportunistic measurements of birds caught in the framework of the AI sampling campaign 2006. This dataset links with 6 other sites in the Russian Far East, and is compatible to other bird banding studies along the flyway. It represents for the first time a coordinated study done during fall migration and with identical methods at 6 sites within the Sea of Okhotsk region, Russian Far East. Data from the other study sites are also available online. An analsis of these data is coming forward; for details please contact authors and project collaborators.
The application of machine learning algorithms in ecology has surged forward in the past two deca... more The application of machine learning algorithms in ecology has surged forward in the past two decades. More and more, we are seeing innovative and interesting uses of these sophisticated algorithms which are driving inference and understanding in natural resource management. The concept behind machine learning is to provide data to a computer and allow the machine to ‘learn’ the patterns in those data. These learned relationships are applied and analysed in a variety of ways from clustering to prediction. In ecology and natural resource management, these methods are not well taught in classroom settings, which is leading to a major disconnect between ecologists and the latest analytical techniques. In this chapter, we introduce machine learning with a focus on ecological and natural resource management applications. We provide definitions and a list of a few key algorithms that are becoming commonplace in the analysis of wildlife data. We further introduce a few broad concepts (i.e., data sharing, metadata and citizen science) in the sphere of ecological sciences. The ideas presented here will help us to better understand how to apply machine learning for conservation, management, and academic studies. These examples can also be used to teach the next generation of scientists how to best use machine learning algorithms in their own work. Our broader goals here, and elsewhere in this book, is to promote a holistic understanding of our planet through algorithms that can handle many interacting covariates that represent how the world really works.
The future climate models show dramatic decay of the status quo (see Xenarios et al. 2019 for Cen... more The future climate models show dramatic decay of the status quo (see Xenarios et al. 2019 for Central Asia and its mountain regions). This is not only true in all relevant weather metrics (e.g. http://worldclim.org/CMIP5v1) but also in terms of glacier loss (https://www.theguardian.com/environment/2019/jun/19/himalayan-glacier-melting-doubled-since-2000-scientists-reveal), habitat transition and wilderness loss (Huettmann 2017), as well as water problems (Karar 2017; Singh et al. 2018; World Water Council 2018; Craymer 2019) which reach far outside of localized problems promoting conflicts and wars elsewhere. And it links directly with many human-related statistics such as population rise, consumption and loss of language, society and culture (Xu et al. 2018 for the Hindu-Kush Himalaya region; see Hinze 2019 for remote monastries shutting down, lack of young people and missing lifestyle). The classic concept of ‘Sustainable Development’ is not sustainable at all and a new governance scheme is to be found serving humans and the earth better.
The Pallas’s cat (Otoclobus manul) has recently been discovered in the Manang valley of Annapurna... more The Pallas’s cat (Otoclobus manul) has recently been discovered in the Manang valley of Annapurna Conservation Area, Nepal by a citizen scientist and a field biologist of Third Pole Conservancy Mr. Tashi R. Ghale. With camera-trapped images and footage evidence, it has been established that the valley is also inhabited by other predators namely snow leopard (Panthera uncia), Grey wolf (Canis lupus), Golden jackal (Canis aureus), Red fox (Vulpes vulpes), Beech marten (Martin foina) and Mountain weasel (Mustella altaica) besides the Pallas’s cat. The Pallas’s cat has been living in the narrow range within a specialized habitat of south-facing sloped rugged terrain of the Manang valley where the Pikas (Ochotona spp.) are abundant. The major survival threats of this ancient, primitive and elusive small cat in the valley are: habitat degradation, prey-base decline and perhaps diseases and the human-induced climate change which directly affects its sensitive and fragile habitat. Future conservation research should focus on accurate density and population estimation of this cat and its principal prey-base i.e. Pika; and how its habitat-complex has been changing over time. The research on trophic cascades would help to understand how the Pallas’s cat is distributed over time and space as part of the wider carnivore community in the Manang valley.
Massive strides have been made in the fields of machine learning and data mining with new algorit... more Massive strides have been made in the fields of machine learning and data mining with new algorithms developing at incredible rates. These advances have come along in the past two decades and have vastly changed almost every aspect of our lives. Thus, it is nearly impossible to envision what these algorithms will be like by the year 2100 (a benchmark year for climate and therefore, ecological forecasting). New synergies between technology and the environment are bound to occur, and in this chapter we discuss some of the ways that could happen, while visiting some of the advancements in machine learning to date. We put this in the context of ecology and natural resource management and argue for better integration of machine learning methods into the ecological process. We also make some commentary on what machine learning tools and techniques will be critical to ecology now and into the near future. Finally, we highlight the concept of holistic ecological modeling by using more than just environmental parameters (i.e. sea surface temperature, rainfall, etc.…). We suggest an approach that includes environmental, anthropogenic, inter (and intra) species, and cosmic parameters (i.e. length of day, season, solar maxima/minima, etc.…). We believe that holistic ecosystem modeling with machine learning as a guide will greatly improve our ability to manage the natural world, when fast and accurate decisions need to be made.
Much of the Hindu Kush-Himalaya (HKH) represents a vast wilderness area that is still relatively ... more Much of the Hindu Kush-Himalaya (HKH) represents a vast wilderness area that is still relatively little affected by humans and modern industry, thus far. It matters for land, sea, atmosphere and the universe. This area consists of 18 nations; it features app. 30% of the world’s gross domestic product (GDP) and it helps to feed and to sustain the global population of this world. It further affects the world’s weather systems and climate, as well as distribution of wealth and global warfare, but also science and spiritual efforts are affected. This landscape is not just a study box of biogeography but it affects most of humanity instead! Water is an essential and connecting component in this set up, but by now it represents a heavily stressed and poorly considered and managed resource. In the HKH region eleven major rivers of global scale and relevance feed a large range and diversity of landscapes and cultures with fresh water. It is widely used for farming, drinking, irrigation, hydrodams, fishing and for estuary replenishment linking mountain peaks with oceans. Here an overview is provided for the Anthropocene across those 18 nations helping to better understand and manage more holistically this complex Hindu Kush-Himalaya area of relevance for mankind and global well-being.
African swine fever (ASF) is a viral disease, endemic to Africa, that causes high mortality when ... more African swine fever (ASF) is a viral disease, endemic to Africa, that causes high mortality when introduced into domestic pig populations. Since the emergence of p72-genotype II African swine fever virus (ASFV) in Georgia in 2007, an ASF epidemic has been spreading across Europe and many countries in Asia. The epidemic first reached Ukraine in 2012. To better understand the dynamics of spread of ASF in Ukraine, we analyzed spatial and temporal outbreak data reported in Ukraine between 2012 and mid-2023. The highest numbers of outbreaks were reported in 2017 (N = 163) and 2018 (N = 145), with overall peak numbers of ASF outbreaks reported in August (domestic pigs) and January (wild boars). While cases were reported from most of Ukraine, we found a directional spread from the eastern and northern borders towards the western and southern regions of Ukraine. Many of the early outbreaks (before 2016) were adjacent to the border, which is again true for more recent outbreaks in wild boar,...
This chapter summarizes rare information and data on how biodiversity and wildlife were governed ... more This chapter summarizes rare information and data on how biodiversity and wildlife were governed on a landscape scale during and after Rana/Royal times in Nepal. Such governance was virtually practiced since deep time, and it still carries much weight, beyond tradition. It’s probably among the longest-lasting governance schemes for watersheds in the study area of the Hindu Kush-Himalaya region. The wildlife and landscape of the lowland Terai region were historically safe and almost untouched before the Rana regime. That’s because people were scared of living in the Terai region due to the widespread presence of epidemic and fatal malaria. During the Rana regime, huge hunting parties (the infamous ‘shikaars’) by the Royal and Rana families and their foreign guests hunted hundreds of rhino, tiger and other animals in the Terai region. The status of wildlife and their habitats started improving again when the government established a protected areas network from 1973 onwards.
<p>List of modeled small mammal species scientific and common names, their associated Taxon... more <p>List of modeled small mammal species scientific and common names, their associated Taxonomic Serial Number (TSN), the number of presence and absence locations used to train models, the resultant area under the receiver operator characteristic (AUC ROC; 0–1), the % of correctly identified presences (specificity), the % of correctly identified absences (sensitivity) and overall % error across all presences and absences.</p
SummaryDeclines in populations of the Critically Endangered Spoon-billed Sandpiper Calidris pygma... more SummaryDeclines in populations of the Critically Endangered Spoon-billed Sandpiper Calidris pygmaeus have been rapid, with the breeding population now perhaps numbering fewer than 120 pairs. The reasons for this decline remain unresolved. Whilst there is evidence that hunting in wintering areas is an important factor, loss of suitable habitat on passage and wintering areas is also of concern. While some key sites for the species are already documented, many of their wintering locations are described here for the first time. Their wintering range primarily stretches from Bangladesh to China. Comprehensive surveys of potential Spoon-billed Sandpiper wintering sites from 2005 to 2013 showed a wide distribution with three key concentrations in Myanmar and Bangladesh, but also regular sites in China, Vietnam and Thailand. The identification of all important non-breeding sites remains of high priority for the conservation of the species. Here, we present the results of field surveys of wi...
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