Search

Your search keyword '"Rowcliffe, J. Marcus"' showing total 372 results
Start Over Author "Rowcliffe, J. Marcus"
372 results on '"Rowcliffe, J. Marcus"'

1. Thresholds for adding degraded tropical forest to the conservation estate

Author

Ewers, Robert M., Orme, C. David L., Pearse, William D., Zulkifli, Nursyamin, Yvon-Durocher, Genevieve, Yusah, Kalsum M., Yoh, Natalie, Yeo, Darren C. J., Wong, Anna, Williamson, Joseph, Wilkinson, Clare L., Wiederkehr, Fabienne, Webber, Bruce L., Wearn, Oliver R., Wai, Leona, Vollans, Maisie, Twining, Joshua P., Turner, Edgar C., Tobias, Joseph A., Thorley, Jack, Telford, Elizabeth M., Teh, Yit Arn, Tan, Heok Hui, Swinfield, Tom, Svátek, Martin, Struebig, Matthew, Stork, Nigel, Sleutel, Jani, Slade, Eleanor M., Sharp, Adam, Shabrani, Adi, Sethi, Sarab S., Seaman, Dave J. I., Sawang, Anati, Roxby, Gabrielle Briana, Rowcliffe, J. Marcus, Rossiter, Stephen J., Riutta, Terhi, Rahman, Homathevi, Qie, Lan, Psomas, Elizabeth, Prairie, Aaron, Poznansky, Frederica, Pillay, Rajeev, Picinali, Lorenzo, Pianzin, Annabel, Pfeifer, Marion, Parrett, Jonathan M., Noble, Ciar D., Nilus, Reuben, Mustaffa, Nazirah, Mullin, Katherine E., Mitchell, Simon, Mckinlay, Amelia R., Maunsell, Sarah, Matula, Radim, Massam, Michael, Martin, Stephanie, Malhi, Yadvinder, Majalap, Noreen, Maclean, Catherine S., Mackintosh, Emma, Luke, Sarah H., Lewis, Owen T., Layfield, Harry J., Lane-Shaw, Isolde, Kueh, Boon Hee, Kratina, Pavel, Konopik, Oliver, Kitching, Roger, Kinneen, Lois, Kemp, Victoria A., Jotan, Palasiah, Jones, Nick, Jebrail, Evyen W., Hroneš, Michal, Heon, Sui Peng, Hemprich-Bennett, David R., Haysom, Jessica K., Harianja, Martina F., Hardwick, Jane, Gregory, Nichar, Gray, Ryan, Gray, Ross E. J., Granville, Natasha, Gill, Richard, Fraser, Adam, Foster, William A., Folkard-Tapp, Hollie, Fletcher, Robert J., Fikri, Arman Hadi, Fayle, Tom M., Faruk, Aisyah, Eggleton, Paul, Edwards, David P., Drinkwater, Rosie, Dow, Rory A., Döbert, Timm F., Didham, Raphael K., Dickinson, Katharine J. M., Deere, Nicolas J., de Lorm, Tijmen, Dawood, Mahadimenakbar M., Davison, Charles W., Davies, Zoe G., Davies, Richard G., Dančák, Martin, Cusack, Jeremy, Clare, Elizabeth L., Chung, Arthur, Chey, Vun Khen, Chapman, Philip M., Cator, Lauren, Carpenter, Daniel, Carbone, Chris, Calloway, Kerry, Bush, Emma R., Burslem, David F. R. P., Brown, Keiron D., Brooks, Stephen J., Brasington, Ella, Brant, Hayley, Boyle, Michael J. W., Both, Sabine, Blackman, Joshua, Bishop, Tom R., Bicknell, Jake E., Bernard, Henry, Basrur, Saloni, Barclay, Maxwell V. L., Barclay, Holly, Atton, Georgina, Ancrenaz, Marc, Aldridge, David C., Daniel, Olivia Z., Reynolds, Glen, and Banks-Leite, Cristina

Published
2024
Full Text
View/download PDF

6. Using water‐landing, fixed‐wing UAVs and computer vision to assess seabird nutrient subsidy effects on sharks and rays.

Author

Schiele, Melissa, Rowcliffe, J. Marcus, Clark, Ben, Lepper, Paul, and Letessier, Tom B.

Subjects
COMPUTER vision, RAYS (Fishes), SHARKS, CORAL reefs & islands, COLONIAL birds
Abstract

Bird colonies on islands sustain elevated productivity and biomass on adjacent reefs, through nutrient subsidies. However, the implications of this localized enhancement on higher and often more mobile trophic levels (such as sharks and rays) are unclear, as spatial trends in mobile fauna are often poorly captured by traditional underwater visual surveys. Here, we explore whether the presence of seabird colonies is associated with enhanced abundances of sharks and rays on adjacent coral reefs. We used a novel long‐range water‐landing fixed‐wing unoccupied aerial vehicle (UAV) to survey the distribution and density of sharks, rays and any additional megafauna, on and around tropical coral islands (n = 14) in the Chagos Archipelago Marine Protected Area. We developed a computer‐vision algorithm to distinguish greenery (trees and shrubs), sand and sea glitter from visible ocean to yield accurate marine megafauna density estimation. We detected elevated seabird densities over rat‐free islands, with the commonest species, sooty tern, reaching densities of 932 ± 199 per km−2 while none were observed over former coconut plantation islands. Elasmobranch density around rat‐free islands with seabird colonies was 6.7 times higher than around islands without seabird colonies (1.3 ± 0.63 vs. 0.2 ± SE 0.1 per km2). Our results are evidence that shark and ray distribution is sensitive to natural and localized nutrient subsidies. Correcting for non‐sampled regions of images increased estimated elasmobranch density by 14%, and our openly accessible computer vision algorithm makes this correction easy to implement to generate shark and ray and other wildlife densities from any aerial imagery. The water‐landing fixed‐wing long‐range UAV technology used in this study may provide cost effective monitoring opportunities in remote ocean locations. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

26. Development of an app for processing data on wildlife density in the field

Author

ENETWILD-consortium, Blanco-Aguiar, José Antonio, Acevedo, Pelayo, Apollonio, Marco, Carniato, D., Casaer, J., Ferroglio, Ezio, Guerrasio, Tancredi, Gomez Molina, A., Janssen, René, Illanas, Sonia, Laguna, Eduardo, Liefting, Y., Keuling, Oliver, Palencia, Pablo, Preite, L., Plis, Kamila, Podgórski, Tomasz, Rowcliffe, J. Marcus, Ruiz-Rodríguez, Carmen, Sebastian, M., Smith, Graham C., Scandura, Massimo, Sereno-Cadierno, Jorge, Soriguer, Ramón C., Vada, Rachele, Zanet, Stefania, Vicente, Joaquín, and European Food Safety Authority

Subjects
Spatial Monitoring and Reporting Tool (SMART), Camera traps, General Engineering, Field protocol, Cloud-based, Abundance estimation, Distance sampling
Abstract

It is essential to provide tools to wildlife professionals and researchers in order to facilitate data collection on wildlife density estimation following standardized protocols in the field. This is relevant for efficient harmonized data management systems, from the field to final reporting. Our main objective was to facilitate the collection of information in the field using established density estimation protocols. The specific objectives were (i) to evaluate and use already existing data registration IT tools for collecting and storing the data in the field; (ii) to make these data available in real time (cloud-based solution), and (iii) being flexible enough to incorporate new protocols and species, as methods (such as camera trap-based) and needs continuously evolves. We improved an already existing tool, Spatial Monitoring and Reporting Tool (SMART; https://smartconservationtools.org/). It is an open source software, which allows easily collect, visualize, store, analyze, report and act on a wide range of field data relevant for wildlife monitoring. The integration of SMART tools on EOW was successfully done for (i) distance sampling, (ii) hunting data and (iii) camera trap protocols. ENETWILD, therefore, made now available new IT functionalities to wildlife professionals and researchers to facilitate and harmonize wildlife data collection systems., EFSA-Q-2022-00044

Published
2022
Full Text
View/download PDF

34. Estimating animal density for a community of species using information obtained only from camera‐traps

Author

Wearn, Oliver R., Bell, Thomas E. M., Bolitho, Adam, Durrant, James, Haysom, Jessica K., Nijhawan, Sahil, Thorley, Jack, Rowcliffe, J. Marcus, Wearn, Oliver R., Bell, Thomas E. M., Bolitho, Adam, Durrant, James, Haysom, Jessica K., Nijhawan, Sahil, Thorley, Jack, and Rowcliffe, J. Marcus

Abstract

1. Animal density is a fundamental parameter in ecology and conservation, and yet it has remained difficult to measure. For terrestrial mammals and birds, camera-traps have dramatically improved our ability to collect systematic data across a large number of species, but density estimation (except for species with natural marks) is still faced with statistical and logistical hurdles, including the requirement for auxiliary data and large sample sizes, and an inability to incorporate covariates. 2. To fill this gap in the camera-trapper's statistical toolbox, we extended the ex-isting Random Encounter Model (REM) to the multi-species case in a Bayesian framework. This multi-species REM can incorporate covariates and provides pa-rameter estimates for even the rarest species. As input to the model, we used information directly available in the camera-trap data. The model outputs poste-rior distributions for the REM parameters—movement speed, activity level, the effective angle and radius of the camera-trap detection zone, and density—for each species. We applied this model to an existing dataset for 35 species in Borneo, collected across old- growth and logged forest. Here, we added animal position data derived from the image sequences in order to estimate the speed and detection zone parameters. 3. The model revealed a decrease in movement speeds, and therefore day- range, across the species community in logged compared to old- growth forest, whilst activity levels showed no consistent trend. Detection zones were shorter, but of similar width, in logged compared to old- growth forest. Overall, animal density was lower in logged forest, even though most species individually occurred at higher density in logged forest. However, the biomass per unit area was sub-stantially higher in logged compared to old- growth forest, particularly among herbivores and omnivores, likely because of increased resource availability at ground level. We also included body mass as a variable

Published
2022

35. Development of an app for processing data on wildlife density in the field

Author

European Food Safety Authority, ENETWILD-consortium, Blanco-Aguiar, José Antonio, Acevedo, Pelayo, Apollonio, Marco, Carniato, Davide, Casaer, Jim, Ferroglio, Ezio, Guerrasio, Tancredi, Gómez-Molina, Azahara, Janssen, René, Illanas, Sonia, Laguna, Eduardo, Liefting, Y., Keuling, Oliver, Palencia, Pablo, Preite, Ludovica, Plis, Kamila, Podgórski, Tomasz, Rowcliffe, J. Marcus, Ruiz-Rodríguez, Carmen, Sebastian, M., Smith, Graham C., Scandura, Massimo, Sereno-Cadierno, Jorge, Soriguer, Ramón C., Vada, Rachele, Zanet, Stefania, Vicente, Joaquín, European Food Safety Authority, ENETWILD-consortium, Blanco-Aguiar, José Antonio, Acevedo, Pelayo, Apollonio, Marco, Carniato, Davide, Casaer, Jim, Ferroglio, Ezio, Guerrasio, Tancredi, Gómez-Molina, Azahara, Janssen, René, Illanas, Sonia, Laguna, Eduardo, Liefting, Y., Keuling, Oliver, Palencia, Pablo, Preite, Ludovica, Plis, Kamila, Podgórski, Tomasz, Rowcliffe, J. Marcus, Ruiz-Rodríguez, Carmen, Sebastian, M., Smith, Graham C., Scandura, Massimo, Sereno-Cadierno, Jorge, Soriguer, Ramón C., Vada, Rachele, Zanet, Stefania, and Vicente, Joaquín

Abstract

It is essential to provide tools to wildlife professionals and researchers in order to facilitate data collection on wildlife density estimation following standardized protocols in the field. This is relevant for efficient harmonized data management systems, from the field to final reporting. Our main objective was to facilitate the collection of information in the field using established density estimation protocols. The specific objectives were (i) to evaluate and use already existing data registration IT tools for collecting and storing the data in the field; (ii) to make these data available in real time (cloud-based solution), and (iii) being flexible enough to incorporate new protocols and species, as methods (such as camera trap-based) and needs continuously evolves. We improved an already existing tool, Spatial Monitoring and Reporting Tool (SMART; https://smartconservationtools.org/). It is an open source software, which allows easily collect, visualize, store, analyze, report and act on a wide range of field data relevant for wildlife monitoring. The integration of SMART tools on EOW was successfully done for (i) distance sampling, (ii) hunting data and (iii) camera trap protocols. ENETWILD, therefore, made now available new IT functionalities to wildlife professionals and researchers to facilitate and harmonize wildlife data collection systems.

Published
2022

36. Remote sensing and the UN Ocean Decade : high expectations, big opportunities

Author

Lecours, Vincent, Disney, Mathias, He, Kate, Pettorelli, Nathalie, Rowcliffe, J. Marcus, Sankey, Temuulen, Scales, Kylie, Lecours, Vincent, Disney, Mathias, He, Kate, Pettorelli, Nathalie, Rowcliffe, J. Marcus, Sankey, Temuulen, and Scales, Kylie

Abstract

This year officially marks the beginning of the United Nations Decade of Ocean Science for Sustainable Development (2021–2030)—the Ocean Decade. A primary objective of this coordination framework is to support scientific research and technological developments that can contribute to the conservation and sustainable management of the world’s oceans. One of the seven Decade Outcomes is to secure healthy and resilient oceans where marine biodiversity is mapped and protected; however, fulfilling this goal will require data, knowledge, and technology. The use of remote sensing is now established in marine research and management and is crucial in developing our understanding of ocean patterns and processes at multiple spatial and temporal scales (e.g., Jawak et al., 2015). As such, remote sensing technology is expected to play a critical role in achieving the vision set by the Ocean Decade.

Published
2022
Full Text
View/download PDF

42. Evidence of deterrence from patrol data: Trialling application of a differenced‐ CPUE metric

Author

Dancer, Anthony, primary, Keane, Aidan, additional, Beale, Colin M., additional, Dobson, Andrew D. M., additional, Amin, Rajan, additional, Freeman, Robin, additional, Imong, Inaoyom, additional, Jones, Kate, additional, Linkie, Matthew, additional, Long, Barney, additional, Okeke, Francis O., additional, Plumptre, Andrew J., additional, Rowcliffe, J. Marcus, additional, Stokes, Emma J., additional, van der Westhuizen, Elsabé, additional, and Collen, Ben, additional

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results