Zobrazeno 1 - 10 of 15 pro vyhledávání: '"Matt Amos"'
1
Akademický článek
Flyway‐scale analysis reveals that the timing of migration in wading birds is becoming later
Autor: Thomas O. Mondain‐Monval, Matt Amos, Jamie‐Leigh Chapman, Andrew MacColl, Stuart P. Sharp
Publikováno v: Ecology and Evolution, Vol 11, Iss 20, Pp 14135-14145 (2021)
Abstract Understanding the implications of climate change for migratory animals is paramount for establishing how best to conserve them. A large body of evidence suggests that birds are migrating earlier in response to rising temperatures, but many s
Externí odkaz: https://doaj.org/article/a5454993f8a644d6bf26b7815588da73
Zobrazit plný text záznamu
3
Home ranges of rusa deer (
Autor: Matt Amos, Anthony Pople, Michael Brennan, Darren Sheil, Mark Kimber, Anthony Cathcart
Publikováno v: Australian Mammalogy. 45:116-120
Wild rusa deer (Cervus timorensis) are increasing in numbers and distribution in peri-urban eastern Australia. To effectively manage rusa deer, land managers need to know the extent of their movements to determine the appropriate scale of control thr
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f486b9bc2e783d3e85ba75d8af3c43c4
https://doi.org/10.1071/am21052
Zobrazit plný text záznamu
4
Estimating deer density and abundance using spatial mark–resight models with camera trap data
Autor: Andrew J Bengsen, David M Forsyth, Dave S L Ramsey, Matt Amos, Michael Brennan, Anthony R Pople, Sebastien Comte, Troy Crittle
Publikováno v: Journal of Mammalogy. 103:711-722
Globally, many wild deer populations are actively studied or managed for conservation, hunting, or damage mitigation purposes. These studies require reliable estimates of population state parameters, such as density or abundance, with a level of prec
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e51ff4f634a20f4ec6dcf0bc9f544de0
https://doi.org/10.1093/jmammal/gyac016
Zobrazit plný text záznamu
5
LancasterAQ: A High Resolution Street Level Dataset of Ultrafine Particles
Autor: Matt Amos, Douglas Booker, Rachael Duncan, Lily Gouldsbrough, Thomas Pinder, Paul Young, Jeremy Carter
We present a mobile dataset of ultrafine particles (UFPs) in Lancaster, UK, with measurements taken by car and bike over 5 days in May 2022. UFPs are a constituent of air pollution and comprise of particulate matter (PM) less than 0.1μm in diameter.
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::67004068a85f62a189d4bfbdadd30aef
https://doi.org/10.31223/x5664v
Zobrazit plný text záznamu
7
A continuous vertically resolved ozone dataset from the fusion of chemistry climate models with observations using a Bayesian neural network
Autor: Matt Amos, Ushnish Sengupta, Paul Young, J. Hosking
Continuous historic datasets of vertically resolved stratospheric ozone, support the case for ozone recovery, are necessary for the running of offline models and increase understanding of the impacts of ozone on the wider atmospheric system. Vertical
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::596c17c7db9e8e95ffbb04b0bef77def
https://doi.org/10.31223/x5n91s
Zobrazit plný text záznamu
8
Flyway-scale analysis reveals that the timing of migration in wading birds is becoming later
Autor: Stuart P. Sharp, Matt Amos, Thomas O. Mondain-Monval, Andrew D. C. MacColl, Jamie-Leigh Chapman
Publikováno v: Ecology and Evolution, Vol 11, Iss 20, Pp 14135-14145 (2021)
Ecology and Evolution
Understanding the implications of climate change for migratory animals is paramount for establishing how best to conserve them. A large body of evidence suggests that birds are migrating earlier in response to rising temperatures, but many studies fo
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a6a4d97efd550007c1277cfba48621ea
https://doi.org/10.1002/ece3.8130
Zobrazit plný text záznamu
9
Fusing model ensembles and observations together with Bayesian neural networks
Autor: Scott Hosking, Paul Young, Ushnish Sengupta, Matt Amos
To fuse together output from ensembles of climate models with observations, we have developed a custom Bayesian neural network that produces more accurate and uncertainty aware projections.Ensembles of physical models are typically used to increase t
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6cad293c47106f2bd6358642125a71f3
https://doi.org/10.5194/egusphere-egu21-11905
Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání

Omezení vyhledávání
Plný text Recenzováno Digitální knihovna AV ČR
Zdroje