Cetacean bycatch in Indian Ocean tuna gillnet fisheries

Pelagic gillnet (driftnet) fisheries account for some 34% of Indian Ocean tuna catches. We combined published results from 10 bycatch sampling programmes (1981−2016) in Australia, Sri Lanka, India and Pakistan to estimate bycatch rates for cetaceans across all Indian Ocean tuna gillnet fisheries. Estimated cetacean bycatch peaked at almost 100 000 ind. yr−1 during 2004−2006, but has declined by over 15% since then, despite an increase in tuna gillnet fishing effort. These fisheries caught an estimated cumulative total of 4.1 million small cetaceans between 1950 and 2018. These bycatch estimates take little or no account of cetaceans caught by gillnet but not landed, of delayed mortality or sub-lethal impacts on cetaceans (especially whales) that escape from gillnets, of mortality associated with ghost nets, of harpoon catches made from gillnetters, or of mortality from other tuna fisheries. Total cetacean mortality from Indian Ocean tuna fisheries may therefore be substantially higher than estimated here. Declining cetacean bycatch rates suggest that such levels of mortality are not sustainable. Indeed, mean small cetacean abundance may currently be 13% of pre-fishery levels. None of these estimates are precise, but they do demonstrate the likely order of magnitude of the issue. Countries with the largest current gillnet catches of tuna, and thus the ones likely to have the largest cetacean bycatch are (in order): Iran, Indonesia, India, Sri Lanka, Pakistan, Oman, Yemen, UAE and Tanzania. These 9 countries together may account for roughly 96% of all cetacean bycatch from tuna gillnet fisheries across the Indian Ocean

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

Cetacean Species Richness and Relative Abundance Around the Bar Reef Marine Sanctuary, Sri Lanka

Volume: 105Start Page: 274End Page: 27

Rediscovering The Dugong (Dugong Dugon) In Myanmar And Capacity Building For Research And Conservation

Ilangakoon, A. D., Tun, Tint (2007): Rediscovering The Dugong (Dugong Dugon) In Myanmar And Capacity Building For Research And Conservation. Raffles Bulletin of Zoology 55 (1): 195-199, DOI: http://doi.org/10.5281/zenodo.533293

Not Available

Not AvailablePelagic gillnet (driftnet) fisheries account for some 34% of Indian Ocean tuna catches. We combined published results from 10 bycatch sampling programmes (1981−2016) in Australia, Sri Lanka, India and Pakistan to estimate bycatch rates for cetaceans across all Indian Ocean tuna gillnet fisheries. Estimated cetacean bycatch peaked at almost 100 000 ind. yr−1 during 2004−2006, but has declined by over 15% since then, despite an increase in tuna gillnet fishing effort. These fisheries caught an estimated cumulative total of 4.1 million small cetaceans between 1950 and 2018. These bycatch estimates take little or no account of cetaceans caught by gillnet but not landed, of delayed mortality or sub-lethal impacts on cetaceans (especially whales) that escape from gillnets, of mortality associated with ghost nets, of harpoon catches made from gillnetters, or of mortality from other tuna fisheries. Total cetacean mortality from Indian Ocean tuna fisheries may therefore be substantially higher than estimated here. Declining cetacean bycatch rates suggest that such levels of mortality are not sustainable. Indeed, mean small cetacean abundance may currently be 13% of pre-fishery levels. None of these estimates are precise, but they do demonstrate the likely order of magnitude of the issue. Countries with the largest current gillnet catches of tuna, and thus the ones likely to have the largest cetacean bycatch are (in order): Iran, Indonesia, India, Sri Lanka, Pakistan, Oman, Yemen, UAE and Tanzania. These 9 countries together may account for roughly 96% of all cetacean bycatch from tuna gillnet fisheries across the Indian Ocean.Not Availabl

Regional scale dryland vegetation classification with an integrated lidar-hyperspectral approach

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM's sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems

Past and present distribution, densities and movements of blue whales \u3ci\u3eBalaenoptera musculus \u3c/i\u3ein the Southern Hemisphere and northern Indian Ocean

1. Blue whale locations in the Southern Hemisphere and northern Indian Ocean were obtained from catches (303 239), sightings (4383 records of ≥ 8058 whales), strandings (103), Discovery marks (2191) and recoveries (95), and acoustic recordings. 2. Sighting surveys included 7 480 450 km of effort plus 14 676 days with unmeasured effort. Groups usually consisted of solitary whales (65.2%) or pairs (24.6%); larger feeding aggregations of unassociated individuals were only rarely observed. Sighting rates (groups per 1000 km from many platform types) varied by four orders of magnitude and were lowest in the waters of Brazil, South Africa, the eastern tropical Pacific, Antarctica and South Georgia; higher in the Subantarctic and Peru; and highest around Indonesia, Sri Lanka, Chile, southern Australia and south of Madagascar. 3. Blue whales avoid the oligotrophic central gyres of the Indian, Pacific and Atlantic Oceans, but are more common where phytoplankton densities are high, and where there are dynamic oceanographic processes like upwelling and frontal meandering. 4. Compared with historical catches, the Antarctic (‘true’) subspecies is exceedingly rare and usually concentrated closer to the summer pack ice. In summer they are found throughout the Antarctic; in winter they migrate to southern Africa (although recent sightings there are rare) and to other northerly locations (based on acoustics), although some overwinter in the Antarctic. 5. Pygmy blue whales are found around the Indian Ocean and from southern Australia to New Zealand. At least four groupings are evident: northern Indian Ocean, from Madagascar to the Subantarctic, Indonesia to western and southern Australia, and from New Zealand northwards to the equator. Sighting rates are typically much higher than for Antarctic blue whales

Harnessing the NEON data revolution to advance open environmental science with a diverse and data‐capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation — across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

Past and present distribution, densities and movements of blue whales Balaenoptera musculus in the Southern Hemisphere and northern Indian Ocean

1. Blue whale locations in the Southern Hemisphere and northern Indian Ocean were obtained from catches (303 239), sightings (4383 records of 8058 whales), strandings (103), Discovery marks (2191) and recoveries (95), and acoustic recordings. 2. Sighting surveys included 7 480 450 km of effort plus 14 676 days with unmeasured effort. Groups usually consisted of solitary whales (65.2%) or pairs (24.6%); larger feeding aggregations of unassociated individuals were only rarely observed. Sighting rates (groups per 1000 km from many platform types) varied by four orders of magnitude and were lowest in the waters of Brazil, South Africa, the eastern tropical Pacific, Antarctica and South Georgia; higher in the Subantarctic and Peru; and highest around Indonesia, Sri Lanka, Chile, southern Australia and south of Madagascar. 3. Blue whales avoid the oligotrophic central gyres of the Indian, Pacific and Atlantic Oceans, but are more common where phytoplankton densities are high, and where there are dynamic oceanographic processes like upwelling and frontal meandering. 4. Compared with historical catches, the Antarctic (‘true’) subspecies is exceedingly rare and usually concentrated closer to the summer pack ice. In summer they are found throughout the Antarctic; in winter they migrate to southern Africa (although recent sightings there are rare) and to other northerly locations (based on acoustics), although some overwinter in the Antarctic. 5. Pygmy blue whales are found around the Indian Ocean and from southern Australia to New Zealand. At least four groupings are evident: northern Indian Ocean, from Madagascar to the Subantarctic, Indonesia to western and southern Australia, and from New Zealand northwards to the equator. Sighting rates are typically much higher than for Antarctic blue whales