Search

Your search keyword '"Gibb, Rory"' showing total 12 results
Start Over Author "Gibb, Rory" Database OpenAIRE
12 results on '"Gibb, Rory"'

1. MAMMALS IN PORTUGAL : A data set of terrestrial, volant, and marine mammal occurrences in P ortugal

Author

Grilo, Clara, Afonso, Beatriz C., Afonso, Filipe, Alexandre, Marta, Aliácar, Sara, Almeida, Ana, Alonso, Ivan Prego, Álvares, Francisco, Alves, Paulo, Alves, Paulo Célio, Alves, Pedro, Duarte, Beatriz, Duro, Virginia, Encarnação, Cláudia, Eufrázio, Sofia, Fael, António, Falé, João Salvador, Faria, Sandra, Fernandes, C, Fernandes, Margarida, Lopes, Susana, Rosalino, L. M., Costa, Gonçalo Ferrão, Ferreira, Clara, Ferreira, Diogo F., Ferreira, Eduardo, Ferreira, Joaquim Pedro, Ferreira, João, Ferreira, Diana, Fonseca, Carlos, Fontes, Inês, Fragoso, Ricardo, Rosário, Inês T., López-Baucells, Adrià, Franco, Claudia, Freitas, Tamira, Gabriel, Sofia I., Gibb, Rory, Gil, Patricia, Gomes, Carla Patricia Jorge, Horta, Pedro, Gomes, Pedro, Gomes, Verónica, Rossa, Mariana, Grilo, Ana Filipa, Amado, Anabela, Guedes, Américo, Guilherme, Filipa, Gutiérrez, Iván, Harper, Henry, Herrera, José M., Hipólito, Dário, Infante, Samuel, Jesus, José, Russo, Danilo, Loureiro, Armando, Loureiro, Filipa, Lourenço, Rui, Lourenço, Sofia, Lucas, Paula, Magalhães, Ana, Maldonado, Cristina, Marcolin, Fabio, Marques, Sara, Marques, J. Tiago, Sá, Pedro, Amendoeira, Vitor, Marques, Carina, Marques, Paulo, Marrecas, Pedro Caetano, Martins, Frederico, Martins, Raquel, Mascarenhas, Miguel, Mata, Vanessa A., Mateus, Ana Rita, Matos, Milene, Sabino‐Marques, Helena, Medinas, Denis, Amorim, Francisco, Mendes, Tiago, Mendes, Gabriel, Mestre, Frederico, Milhinhas, Catarina, Mira, António, Monarca, Rita I., Monteiro, Norberto, Monteiro, Barbara, Salgueiro, Vânia, Monterroso, Pedro, Nakamura, Mónia, Silva Aparício, Guilherme, Negrões, Nuno, Nóbrega, Eva K., Nóvoa, Miguel, Nunes, Manuel, Nunes, Nuno Jardim, Oliveira, Flávio, Oliveira, José Miguel, Santos, Helena, M. Palmeirim, Jorge, Pargana, João, Paula, Anabela, Araújo, Ricardo, Paupério, Joana, Pedroso, Nuno M., Pereira, Guilherme, Pereira, Pedro F., Pereira, José, Pereira, Maria João Ramos, Santos, Joana, Petrucci-Fonseca, Francisco, Pimenta, Miguel, Pinto, Sara, Pinto, Nuno, Ascensão, Fernando, Pires, Rosa, Pita, Ricardo, Pontes, Carlos, Quaresma, Marisa, Queirós, João, Barbosa, A. Márcia, Queirós, Luís, Rainho, Ana, Graça Ramalhinho, Maria, Ramalho, Patrícia, Raposeira, Helena, Augusto, Margarida, Rasteiro, Francisco, Rebelo, Hugo, Regala, Frederico Tátá, Reto, Dyana, Jones, Kate E., Ribeiro, Sérgio Bruno, Rio‐Maior, Helena, Rocha, Ricardo, Rocha, Rita Gomes, Rodrigues, Luísa, Román, Jacinto, Bandeira, Victor, Roque, Sara, Santos, João P. V., Santos, Nuno, Santos, Sara, Santos, Carlos Pedro, Santos-Reis, Margarida, Serronha, Ana, Sierra, Pablo, Silva, Bruno, Silva, Carla S. G. M., Silva, Clara, Laborde, Marina I., Barbosa, Soraia, Silva, Diogo, Silva, Luís P., Silva, Ricardo, Silva, Carmen, Silva Júnior, Flavio Manoel Rodrigues, Sousa, Pedro, Sousa‐Guedes, Diana, Spadoni, Giulia, Tapisso, Joaquim T., Oliveira, Luís Lamas, Teixeira, Daniela, Barbosa, Sérgio, Teixeira, Sérgio, Teixeira, Nuno, Torres, Rita T., Travassos, Paulo, Vale‐Gonçalves, Hélia, Cidraes‐Vieira, Nuno, Merten, Sophie, Luz Mathias, Maria, Leitão, Inês, Barreiro, Silvia, Barros, Paulo, Barros, Tânia, Barros, Filomena, Pinto Basto, Mafalda, Bernardino, Joana, Bicho, Sara, Biedma, Luis Eduardo, Borges, Marta, Braz, Luis, Lemos, Rita, Brito, José Carlos, Brito, Tiago, Cabral, João Alexandre, Calzada, Javier, Camarinha, Cláudia, Carapuço, Mafalda, Cardoso, Paulo, Carmo, Mário, Carrapato, Carlos, Silva Carrilho, Maílis, Lima, Cátia, Carvalho, Diogo Filipe T. C. S., Carvalho, Filipe, Carvalho, João, Castro, Diana, Castro, Guilherme, Castro, Joana, Castro, Luis Roma, Catry, Filipe Xavier, Cerveira, Ana M., Cid, André, Linck, Paloma, Clarke, Rafael, Conde, Conceição, Conde, José, Costa, Jorge, Costa, Mafalda, Costa, Pedro, Costa, Cristina, Couto, André Pedro, Craveiro, João, Dias, Marta, Lopes, Hugo, Dias, Sofia, and Repositório da Universidade de Lisboa

Abstract

Mammals are threatened worldwide, with 26% of all species being includedin the IUCN threatened categories. This overall pattern is primarily associatedwith habitat loss or degradation, and human persecution for terrestrial mam-mals, and pollution, open net fishing, climate change, and prey depletion formarine mammals. Mammals play a key role in maintaining ecosystems func-tionality and resilience, and therefore information on their distribution is cru-cial to delineate and support conservation actions. MAMMALS INPORTUGAL is a publicly available data set compiling unpublishedgeoreferenced occurrence records of 92 terrestrial, volant, and marine mam-mals in mainland Portugal and archipelagos of the Azores and Madeira thatincludes 105,026 data entries between 1873 and 2021 (72% of the data occur-ring in 2000 and 2021). The methods used to collect the data were: live obser-vations/captures (43%), sign surveys (35%), camera trapping (16%),bioacoustics surveys (4%) and radiotracking, and inquiries that represent lessthan 1% of the records. The data set includes 13 types of records: (1) burrowsjsoil moundsjtunnel, (2) capture, (3) colony, (4) dead animaljhairjskullsjjaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8),observation in shelters, (9) photo trappingjvideo, (10) predators dietjpelletsjpine cones/nuts, (11) scatjtrackjditch, (12) telemetry and (13) vocalizationjecholocation. The spatial uncertainty of most records ranges between 0 and100 m (76%). Rodentia (n=31,573) has the highest number of records followedby Chiroptera (n=18,857), Carnivora (n=18,594), Lagomorpha (n=17,496),Cetartiodactyla (n=11,568) and Eulipotyphla (n=7008). The data setincludes records of species classified by the IUCN as threatened(e.g.,Oryctolagus cuniculus[n=12,159],Monachus monachus[n=1,512],andLynx pardinus[n=197]). We believe that this data set may stimulate thepublication of other European countries data sets that would certainly contrib-ute to ecology and conservation-related research, and therefore assisting onthe development of more accurate and tailored conservation managementstrategies for each species. There are no copyright restrictions; please cite thisdata paper when the data are used in publications.

Published
2022

3. Bat detective—Deep learning tools for bat acoustic signal detection

Author

Mac Aodha, Oisin, Gibb, Rory, Barlow, Kate E., Browning, Ella, Firman, Michael, Freeman, Robin, Harder, Briana, Kinsey, Libby, Mead, Gary R., Newson, Stuart E., Pandourski, Ivan, Parsons, Stuart, Russ, Jon, Szodoray-Paradi, Abigel, Szodoray-Paradi, Farkas, Tilova, Elena, Girolami, Mark, Brostow, Gabriel, and Jones, Kate E.

Subjects
Computer and Information Sciences, Neural Networks, Physiology, Audio Signal Processing, Sensory Physiology, Research and Analysis Methods, Machine Learning, Chiroptera, Bats, Acoustic Signals, Medicine and Health Sciences, Animals, Animal Signaling and Communication, Mammals, Behavior, Animal Behavior, Physics, Applied Mathematics, Simulation and Modeling, Endangered Species, Organisms, Biology and Life Sciences, Eukaryota, Computational Biology, Signal Processing, Computer-Assisted, Acoustics, Sensory Systems, Auditory System, Echolocation, Physical Sciences, Vertebrates, Amniotes, Signal Processing, Engineering and Technology, Animal Migration, Neural Networks, Computer, Zoology, Bioacoustics, Mathematics, Algorithms, Research Article, Animal Navigation, Neuroscience, Environmental Monitoring
Abstract

Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio., Author summary There is a critical need for robust and accurate tools to scale up biodiversity monitoring and to manage the impact of anthropogenic change. For example, the monitoring of bat species and their population dynamics can act as an important indicator of ecosystem health as they are particularly sensitive to habitat conversion and climate change. In this work we propose a fully automatic and efficient method for detecting bat echolocation calls in noisy audio recordings. We show that our approach is more accurate compared to existing algorithms and other commercial tools. Our method enables us to automatically estimate bat activity from multi-year, large-scale, audio monitoring programmes.

Published
2018

5. Passive acoustic monitoring in ecology and conservation

Author

Browning, Ella, Gibb, Rory, Glover-Kapfer, Paul, and Jones, Kate E.

Subjects
acoustic backscatter sensors [Instrument Type Vocabulary], Physical oceanography::Acoustics [Parameter Discipline], Acoustic monitoring, Conservation, Field ecology, Ecoacoustics, Instrument Type Vocabulary::acoustic backscatter sensors, Parameter Discipline::Physical oceanography::Acoustics, Acoustic sensors
Abstract

are urgently needed to understand how global change is affecting wildlife and ecosystems. Sound is an important component of any habitat, and sound recordings made in the field offer potentially rich sources of ecological information about the abundance, distribution and behaviour of vocalising animals in an area. Acoustic sensors are therefore becoming widely used in ecology and conservation settings to monitor animal populations, behaviour, and responses to environmental change. In recent years the burgeoning field of ecoacoustics has also begun providing insights into acoustic community dynamics at larger scales. With technological improvements making sophisticated off-the-shelf bioacoustic sensors increasingly affordable, it is an exciting and fast-moving time for acoustic wildlife monitoring. Research in this field is now addressing fundamental questions in ecology and animal behaviour, but is also becoming increasingly useful in applied conservation settings, such as monitoring populations of endangered or data-deficient species, or monitoring illegal activities in high-risk areas. However, despite this rapid growth in potential uses, there remains a lack of best-practice guidelines for researchers wishing to deploy acoustic sensors in the field to address particular questions. This guide seeks to address this gap, by providing an introduction to acoustic monitoring technology and its current and emerging uses in ecology and conservation, alongside clear guidelines for acoustic sensor deployment, survey design and data analysis. Published Refereed Current 14.A Ocean sound TRL 9 Actual system "mission proven" through successful mission operations (ground or space) Manual (incl. handbook, guide, cookbook etc)

Published
2017

7. The future of zoonotic risk prediction

Author

Carlson, Colin J, Farrell, Maxwell J, Grange, Zoe, Han, Barbara A, Mollentze, Nardus, Phelan, Alexandra L, Rasmussen, Angela L, Albery, Gregory F, Bett, Bernard, Brett-Major, David M, Cohen, Lily E, Dallas, Tad, Eskew, Evan A, Fagre, Anna C, Forbes, Kristian M, Gibb, Rory, Halabi, Sam, Hammer, Charlotte C, Katz, Rebecca, Kindrachuk, Jason, Muylaert, Renata L, Nutter, Felicia B, Ogola, Joseph, Olival, Kevin J, Rourke, Michelle, Ryan, Sadie J, Ross, Noam, Seifert, Stephanie N, Sironen, Tarja, Standley, Claire J, Taylor, Kishana, Venter, Marietjie, and Webala, Paul W

Subjects
epidemic risk, viral ecology, Ecology, SARS-CoV-2, global health, COVID-19, Animals, Wild, zoonotic risk, 3. Good health, access and benefit sharing, Machine Learning, Risk Factors, FOS: Biological sciences, Zoonoses, Viruses, Animals, Humans, Laboratories, Pandemics, Disease Reservoirs
Abstract

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

8. The future of zoonotic risk prediction

Author

Carlson, Colin J., Farrell, Maxwell J., Grange, Zoe, Han, Barbara A., Mollentze, Nardus, Phelan, Alexandra L., Rasmussen, Angela L., Albery, Gregory F., Bett, Bernard, Brett-Major, David M., Cohen, Lily E., Dallas, Tad, Eskew, Evan A., Fagre, Anna C., Forbes, Kristian M., Gibb, Rory, Halabi, Sam, Hammer, Charlotte C., Katz, Rebecca, Kindrachuk, Jason, Muylaert, Renata L., Nutter, Felicia B., Ogola, Joseph, Olival, Kevin J., Rourke, Michelle, Ryan, Sadie J., Ross, Noam, Seifert, Stephanie N., Sironen, Tarja, Standley, Claire J., Taylor, Kishana, Venter, Marietjie, and Webala, Paul W.

Subjects
access and benefit sharing, epidemic risk, machine learning, viral ecology, PART III: ZOONOTIC DISEASE RISK AND IMPACTS, global health, zoonotic risk, Opinion piece, 3. Good health
Abstract

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.

9. The future of zoonotic risk prediction

Author

Carlson, Colin J., Farrell, Maxwell J., Grange, Zoe, Han, Barbara A., Mollentze, Nardus, Phelan, Alexandra L., Rasmussen, Angela L., Albery, Gregory F., Bett, Bernard, Brett-Major, David M., Cohen, Lily E., Dallas, Tad, Eskew, Evan A., Fagre, Anna C., Forbes, Kristian M., Gibb, Rory, Halabi, Sam, Hammer, Charlotte C., Katz, Rebecca, Kindrachuk, Jason, Muylaert, Renata L., Nutter, Felicia B., Ogola, Joseph, Olival, Kevin J., Rourke, Michelle, Ryan, Sadie J., Ross, Noam, Seifert, Stephanie N., Sironen, Tarja, Standley, Claire J., Taylor, Kishana, Venter, Marietjie, and Webala, Paul W.

Subjects
access and benefit sharing, epidemic risk, machine learning, viral ecology, PART III: ZOONOTIC DISEASE RISK AND IMPACTS, global health, zoonotic risk, Opinion piece, 3. Good health
Abstract

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.

10. The future of zoonotic risk prediction

Author

Carlson, Colin J., Farrell, Maxwell J., Grange, Zoe, Han, Barbara A., Mollentze, Nardus, Phelan, Alexandra L., Rasmussen, Angela L., Albery, Gregory F., Bett, Bernard, Brett-Major, David M., Cohen, Lily E., Dallas, Tad, Eskew, Evan A., Fagre, Anna C., Forbes, Kristian M., Gibb, Rory, Halabi, Sam, Hammer, Charlotte C., Katz, Rebecca, Kindrachuk, Jason, Muylaert, Renata L., Nutter, Felicia B., Ogola, Joseph, Olival, Kevin J., Rourke, Michelle, Ryan, Sadie J., Ross, Noam, Seifert, Stephanie N., Sironen, Tarja, Standley, Claire J., Taylor, Kishana, Venter, Marietjie, and Webala, Paul W.

Subjects
access and benefit sharing, epidemic risk, machine learning, viral ecology, PART III: ZOONOTIC DISEASE RISK AND IMPACTS, global health, zoonotic risk, Opinion piece, 3. Good health
Abstract

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.

11. The future of zoonotic risk prediction

Author

Michelle Rourke, David M. Brett-Major, Noam Ross, Gregory F. Albery, Maxwell J. Farrell, Evan A. Eskew, Kevin J. Olival, Joseph Ogola, Felicia B. Nutter, Kishana Taylor, Anna C. Fagre, Stephanie N. Seifert, Marietjie Venter, Bernard K. Bett, Renata L. Muylaert, Paul W. Webala, Nardus Mollentze, Alexandra Phelan, Colin J. Carlson, Lily E. Cohen, Sadie J. Ryan, Sam F. Halabi, Rory Gibb, Tad A. Dallas, Angela L. Rasmussen, Barbara A. Han, Rebecca Katz, Claire J. Standley, Tarja Sironen, Kristian M. Forbes, Jason Kindrachuk, Zoe Grange, Charlotte C. Hammer, Carlson, Colin J. [0000-0001-6960-8434], Farrell, Maxwell J. [0000-0003-0452-6993], Han, Barbara A. [0000-0002-9948-3078], Brett-Major, David M. [0000-0002-7583-8495], Dallas, Tad [0000-0003-3328-9958], Eskew, Evan A. [0000-0002-1153-5356], Fagre, Anna C. [0000-0002-0969-5078], Forbes, Kristian M. [0000-0002-2112-2707], Gibb, Rory [0000-0002-0965-1649], Hammer, Charlotte C. [0000-0002-8288-0288], Ryan, Sadie J. [0000-0002-4308-6321], Apollo - University of Cambridge Repository, Helsinki One Health (HOH), Viral Zoonosis Research Unit, Emerging Infections Research Group, Department of Virology, Veterinary Biosciences, Medicum, Carlson, Colin J [0000-0001-6960-8434], Farrell, Maxwell J [0000-0003-0452-6993], Han, Barbara A [0000-0002-9948-3078], Brett-Major, David M [0000-0002-7583-8495], Eskew, Evan A [0000-0002-1153-5356], Fagre, Anna C [0000-0002-0969-5078], Forbes, Kristian M [0000-0002-2112-2707], Hammer, Charlotte C [0000-0002-8288-0288], and Ryan, Sadie J [0000-0002-4308-6321]

Subjects
Disease reservoir, viral ecology, EBOLA, global health, Animals, Wild, Airborne transmission, General Biochemistry, Genetics and Molecular Biology, access and benefit sharing, 03 medical and health sciences, Risk Factors, Political science, Zoonoses, PART III: ZOONOTIC DISEASE RISK AND IMPACTS, Pandemic, SURVEILLANCE, Global health, Animals, SPILLOVER, Pandemics, Opinion piece, 030304 developmental biology, Disease Reservoirs, 11832 Microbiology and virology, 0303 health sciences, Equity (economics), epidemic risk, Ecology, 030306 microbiology, business.industry, SARS-CoV-2, AIRBORNE TRANSMISSION, COVID-19, HUMANS, zoonotic risk, Public relations, 3142 Public health care science, environmental and occupational health, 3. Good health, Open data, machine learning, Infectious disease (medical specialty), DISEASES, Viruses, HIV-1, VIRUS, HOST-RANGE, General Agricultural and Biological Sciences, business, Laboratories
Abstract

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.

Published
2021

12. MAMMALS IN PORTUGAL: A data set of terrestrial, volant, and marine mammal occurrences in Portugal

Author

Clara Grilo, Beatriz C. Afonso, Filipe Afonso, Marta Alexandre, Sara Aliácar, Ana Almeida, Ivan Prego Alonso, Francisco Álvares, Paulo Alves, Paulo Célio Alves, Pedro Alves, Anabela Amado, Vitor Amendoeira, Francisco Amorim, Guilherme Silva Aparício, Ricardo Araújo, Fernando Ascensão, Margarida Augusto, Victor Bandeira, A. Márcia Barbosa, Soraia Barbosa, Sérgio Barbosa, Silvia Barreiro, Paulo Barros, Tânia Barros, Filomena Barros, Mafalda Basto, Joana Bernardino, Sara Bicho, Luis Eduardo Biedma, Marta Borges, Luis Braz, José Carlos Brito, Tiago Brito, João Alexandre Cabral, Javier Calzada, Cláudia Camarinha, Mafalda Carapuço, Paulo Cardoso, Mário Carmo, Carlos Carrapato, Maílis Silva Carrilho, Diogo Filipe T. C. S. Carvalho, Filipe Carvalho, João Carvalho, Diana Castro, Guilherme Castro, Joana Castro, Luis Roma Castro, Filipe Xavier Catry, Ana M. Cerveira, André Cid, Rafael Clarke, Conceição Conde, José Conde, Jorge Costa, Mafalda Costa, Pedro Costa, Cristina Costa, André Pedro Couto, João Craveiro, Marta Dias, Sofia Dias, Beatriz Duarte, Virginia Duro, Cláudia Encarnação, Sofia Eufrázio, António Fael, João Salvador Falé, Sandra Faria, Carlos Fernandes, Margarida Fernandes, Gonçalo Ferrão Costa, Clara Ferreira, Diogo F. Ferreira, Eduardo Ferreira, Joaquim Pedro Ferreira, João Ferreira, Diana Ferreira, Carlos Fonseca, Inês Fontes, Ricardo Fragoso, Claudia Franco, Tamira Freitas, Sofia I. Gabriel, Rory Gibb, Patricia Gil, Carla Patricia Jorge Gomes, Pedro Horta, Pedro Gomes, Verónica Gomes, Filipa Grilo, Américo Guedes, Filipa Guilherme, Iván Gutiérrez, Henry Harper, José M. Herrera, Dário Hipólito, Samuel Infante, José Jesus, Kate E. Jones, Marina I. Laborde, Luís Lamas Oliveira, Inês Leitão, Rita Lemos, Cátia Lima, Paloma Linck, Hugo Lopes, Susana Lopes, Adrià López‐Baucells, Armando Loureiro, Filipa Loureiro, Rui Lourenço, Sofia Lourenço, Paula Lucas, Ana Magalhães, Cristina Maldonado, Fabio Marcolin, Sara Marques, J. Tiago Marques, Carina Marques, Paulo Marques, Pedro Caetano Marrecas, Frederico Martins, Raquel Martins, Miguel Mascarenhas, Vanessa A. Mata, Ana Rita Mateus, Milene Matos, Denis Medinas, Tiago Mendes, Gabriel Mendes, Frederico Mestre, Catarina Milhinhas, António Mira, Rita I. Monarca, Norberto Monteiro, Barbara Monteiro, Pedro Monterroso, Mónia Nakamura, Nuno Negrões, Eva K. Nóbrega, Miguel Nóvoa, Manuel Nunes, Nuno Jardim Nunes, Flávio Oliveira, José Miguel Oliveira, Jorge M. Palmeirim, João Pargana, Anabela Paula, Joana Paupério, Nuno M. Pedroso, Guilherme Pereira, Pedro F. Pereira, José Pereira, Maria João Ramos Pereira, Francisco Petrucci‐Fonseca, Miguel Pimenta, Sara Pinto, Nuno Pinto, Rosa Pires, Ricardo Pita, Carlos Pontes, Marisa Quaresma, João Queirós, Luís Queirós, Ana Rainho, Maria Graça Ramalhinho, Patrícia Ramalho, Helena Raposeira, Francisco Rasteiro, Hugo Rebelo, Frederico Tátá Regala, Dyana Reto, Sérgio Bruno Ribeiro, Helena Rio‐Maior, Ricardo Rocha, Rita Gomes Rocha, Luísa Rodrigues, Jacinto Román, Sara Roque, Luís Miguel Rosalino, Inês T. Rosário, Mariana Rossa, Danilo Russo, Pedro Sá, Helena Sabino‐Marques, Vânia Salgueiro, Helena Santos, Joana Santos, João P. V. Santos, Nuno Santos, Sara Santos, Carlos Pedro Santos, Margarida Santos‐Reis, Ana Serronha, Pablo Sierra, Bruno Silva, Carla S. G. M. Silva, Clara Silva, Diogo Silva, Luís P. Silva, Ricardo Silva, Carmen Silva, Flavio Manoel Rodrigues Silva Júnior, Pedro Sousa, Diana Sousa‐Guedes, Giulia Spadoni, Joaquim T. Tapisso, Daniela Teixeira, Sérgio Teixeira, Nuno Teixeira, Rita T. Torres, Paulo Travassos, Hélia Vale‐Gonçalves, Nuno Cidraes‐Vieira, Sophie Merten, Maria Luz Mathias, Grilo, Clara, Afonso, Beatriz C, Afonso, Filipe, Alexandre, Marta, Aliácar, Sara, Almeida, Ana, Alonso, Ivan Prego, Álvares, Francisco, Alves, Paulo, Alves, Paulo Célio, Alves, Pedro, Amado, Anabela, Amendoeira, Vitor, Amorim, Francisco, da Silva Aparício, Guilherme, Araújo, Ricardo, Ascensão, Fernando, Augusto, Margarida, Bandeira, Victor, Barbosa, A Márcia, Barbosa, Soraia, Barbosa, Sérgio, Barreiro, Silvia, Barros, Paulo, Barros, Tânia, Barros, Filomena, Basto, Mafalda, Bernardino, Joana, Bicho, Sara, Biedma, Luis Eduardo, Borges, Marta, Braz, Lui, Brito, José Carlo, Brito, Tiago, Cabral, João Alexandre, Calzada, Javier, Camarinha, Cláudia, Carapuço, Mafalda, Cardoso, Paulo, Carmo, Mário, Carrapato, Carlo, da Silva Carrilho, Maíli, Carvalho, Diogo Filipe T C S, Carvalho, Filipe, Carvalho, João, Castro, Diana, Castro, Guilherme, Castro, Joana, Castro, Luis Roma, Catry, Filipe Xavier, Cerveira, Ana M, Cid, André, Clarke, Rafael, Conde, Conceição, Conde, José, Costa, Jorge, Costa, Mafalda, Costa, Pedro, Costa, Cristina, do Couto, André Pedro, Craveiro, João, Dias, Marta, Dias, Sofia, Duarte, Beatriz, Duro, Virginia, Encarnação, Cláudia, Eufrázio, Sofia, Fael, António, Falé, João Salvador, Faria, Sandra, Fernandes, Carlo, Fernandes, Margarida, da Costa, Gonçalo Ferrão, Ferreira, Clara, Ferreira, Diogo F, Ferreira, Eduardo, Ferreira, Joaquim Pedro, Ferreira, João, Ferreira, Diana, Fonseca, Carlo, Fontes, Inê, Fragoso, Ricardo, Franco, Claudia, Freitas, Tamira, Gabriel, Sofia I, Gibb, Rory, Gil, Patricia, Gomes, Carla Patricia Jorge, Horta, Pedro, Gomes, Pedro, Gomes, Verónica, Grilo, Filipa, Guedes, Américo, Guilherme, Filipa, Gutiérrez, Iván, Harper, Henry, Herrera, José M, Hipólito, Dário, Infante, Samuel, Jesus, José, Jones, Kate E, Laborde, Marina I, de Oliveira, Luís Lama, Leitão, Inê, Lemos, Rita, Lima, Cátia, Linck, Paloma, Lopes, Hugo, Lopes, Susana, López-Baucells, Adrià, Loureiro, Armando, Loureiro, Filipa, Lourenço, Rui, Lourenço, Sofia, Lucas, Paula, Magalhães, Ana, Maldonado, Cristina, Marcolin, Fabio, Marques, Sara, Marques, J Tiago, Marques, Carina, Marques, Paulo, Marrecas, Pedro Caetano, Martins, Frederico, Martins, Raquel, Mascarenhas, Miguel, Mata, Vanessa A, Mateus, Ana Rita, Matos, Milene, Medinas, Deni, Mendes, Tiago, Mendes, Gabriel, Mestre, Frederico, Milhinhas, Catarina, Mira, António, Monarca, Rita I, Monteiro, Norberto, Monteiro, Barbara, Monterroso, Pedro, Nakamura, Mónia, Negrões, Nuno, Nóbrega, Eva K, Nóvoa, Miguel, Nunes, Manuel, Nunes, Nuno Jardim, Oliveira, Flávio, Oliveira, José Miguel, Palmeirim, Jorge M, Pargana, João, Paula, Anabela, Paupério, Joana, Pedroso, Nuno M, Pereira, Guilherme, Pereira, Pedro F, Pereira, José, Pereira, Maria João Ramo, Petrucci-Fonseca, Francisco, Pimenta, Miguel, Pinto, Sara, Pinto, Nuno, Pires, Rosa, Pita, Ricardo, Pontes, Carlo, Quaresma, Marisa, Queirós, João, Queirós, Luí, Rainho, Ana, da Graça Ramalhinho, Maria, Ramalho, Patrícia, Raposeira, Helena, Rasteiro, Francisco, Rebelo, Hugo, Regala, Frederico Tátá, Reto, Dyana, Ribeiro, Sérgio Bruno, Rio-Maior, Helena, Rocha, Ricardo, Rocha, Rita Gome, Rodrigues, Luísa, Román, Jacinto, Roque, Sara, Rosalino, Luís Miguel, do Rosário, Inês T, Rossa, Mariana, Russo, Danilo, Sá, Pedro, Sabino-Marques, Helena, Salgueiro, Vânia, Santos, Helena, Santos, Joana, Santos, João P V, Santos, Nuno, Santos, Sara, Santos, Carlos Pedro, Santos-Reis, Margarida, Serronha, Ana, Sierra, Pablo, Silva, Bruno, Silva, Carla S G M, Silva, Clara, Silva, Diogo, da Silva, Luís P, Silva, Ricardo, Silva, Carmen, da Silva Júnior, Flavio Manoel Rodrigue, Sousa, Pedro, Sousa-Guedes, Diana, Spadoni, Giulia, Tapisso, Joaquim T, Teixeira, Daniela, Teixeira, Sérgio, Teixeira, Nuno, Torres, Rita T, Travassos, Paulo, Vale-Gonçalves, Hélia, Cidraes-Vieira, Nuno, von Merten, Sophie, and da Luz Mathias, Maria

Subjects
1873-2021, Mammals, EuropeIberian Peninsula, Portugal, Animal, Climate Change, Carnivora, Eulipotyphla, Rodentia, Rabbit, Lagomorpha, Mammal, Europe, Rodentiaspecies distribution, Chiroptera, Animals, species distribution, Rabbits, Cetartiodactyla, Ecology, Evolution, Behavior and Systematics, Ecosystem, Iberian Peninsula
Abstract

Mammals are threatened worldwide, with ~26% of all species being included in the IUCN threatened categories. This overall pattern is primarily associated with habitat loss or degradation, and human persecution for terrestrial mammals, and pollution, open net fishing, climate change, and prey depletion for marine mammals. Mammals play a key role in maintaining ecosystems functionality and resilience, and therefore information on their distribution is crucial to delineate and support conservation actions. MAMMALS IN PORTUGAL is a publicly available data set compiling unpublished georeferenced occurrence records of 92 terrestrial, volant, and marine mammals in mainland Portugal and archipelagos of the Azores and Madeira that includes 105,026 data entries between 1873 and 2021 (72% of the data occurring in 2000 and 2021). The methods used to collect the data were: live observations/captures (43%), sign surveys (35%), camera trapping (16%), bioacoustics surveys (4%) and radiotracking, and inquiries that represent less than 1% of the records. The data set includes 13 types of records: (1) burrows | soil mounds | tunnel, (2) capture, (3) colony, (4) dead animal | hair | skulls | jaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8), observation in shelters, (9) photo trapping | video, (10) predators diet | pellets | pine cones/nuts, (11) scat | track | ditch, (12) telemetry and (13) vocalization | echolocation. The spatial uncertainty of most records ranges between 0 and 100 m (76%). Rodentia (n=31,573) has the highest number of records followed by Chiroptera (n=18,857), Carnivora (n=18,594), Lagomorpha (n=17,496), Cetartiodactyla (n=11,568) and Eulipotyphla (n=7008). The data set includes records of species classified by the IUCN as threatened (e.g., Oryctolagus cuniculus [n=12,159], Monachus monachus [n=1,512], and Lynx pardinus [n=197]). We believe that this data set may stimulate the publication of other European countries data sets that would certainly contribute to ecology and conservation-related research, and therefore assisting on the development of more accurate and tailored conservation management strategies for each species. There are no copyright restrictions; please cite this data paper when the data are used in publications.

Published
2021

Catalog

Books, media, physical & digital resources
See catalog results