Shaun Coutts
Shaun Coutts
Lincoln Institute of Agri-food Technology, University of Lincoln
Verified email at - Homepage
Cited by
Cited by
What are the key drivers of spread in invasive plants: dispersal, demography or landscape: and how can we use this knowledge to aid management?
SR Coutts, RD van Klinken, H Yokomizo, YM Buckley
Biological invasions 13 (7), 1649-1661, 2011
The factors driving evolved herbicide resistance at a national scale
HL Hicks, D Comont, SR Coutts, L Crook, R Hull, K Norris, P Neve, ...
Nature ecology & evolution 2 (3), 529-536, 2018
Less favourable climates constrain demographic strategies in plants
AM Csergő, R Salguero‐Gómez, O Broennimann, SR Coutts, A Guisan, ...
Ecology letters 20 (8), 969-980, 2017
Modeling population dynamics, landscape structure, and management decisions for controlling the spread of invasive plants
P Caplat, S Coutts, YM Buckley
Annals of the New York Academy of Sciences 1249 (1), 72-83, 2012
Meta-models as a straightforward approach to the sensitivity analysis of complex models
SR Coutts, H Yokomizo
Population Ecology 56 (1), 7-19, 2014
The behavior of multiple independent managers and ecological traits interact to determine prevalence of weeds
SR Coutts, H Yokomizo, YM Buckley
Ecological Applications 23 (3), 523-536, 2013
Integrating ecological knowledge, public perception and urgency of action into invasive species management
P Caplat, SR Coutts
Environmental Management 48 (5), 878, 2011
The costs of human-induced evolution in an agricultural system
A Varah, K Ahodo, SR Coutts, HL Hicks, D Comont, L Crook, R Hull, ...
Nature sustainability 3 (1), 63-71, 2020
Considering weed management as a social dilemma bridges individual and collective interests
MV Bagavathiannan, S Graham, Z Ma, JN Barney, SR Coutts, AL Caicedo, ...
Nature plants 5 (4), 343-351, 2019
Invasion lags: The stories we tell ourselves and our inability to infer process from pattern
SR Coutts, KJ Helmstedt, JR Bennett
Diversity and Distributions 24 (2), 244-251, 2018
Extrapolating demography with climate, proximity and phylogeny: approach with caution
SR Coutts, R Salguero‐Gómez, AM Csergő, YM Buckley
Ecology Letters 19 (12), 1429-1438, 2016
Decision science for effective management of populations subject to stochasticity and imperfect knowledge
H Yokomizo, SR Coutts, HP Possingham
Population Ecology 56 (1), 41-53, 2014
Are high-impact species predictable? An analysis of naturalised grasses in northern Australia
RD van Klinken, FD Panetta, SR Coutts
PloS one 8 (7), e68678, 2013
Learning from the past to predict the future: an historical analysis of grass invasions in northern Australia
RD van Klinken, FD Panetta, S Coutts, BK Simon
Biological Invasions 17 (2), 565-579, 2015
Reproductive ecology of Pinus nigra in an invasive population: individual-and population-level variation in seed production and timing of seed release
SR Coutts, P Caplat, K Cousins, N Ledgard, YM Buckley
Annals of forest science 69 (4), 467-476, 2012
Improving private land conservation with outcome‐based biodiversity payments
JA McDonald, KJ Helmstedt, M Bode, S Coutts, E McDonald‐Madden, ...
Journal of Applied Ecology 55 (3), 1476-1485, 2018
Gone with the wind: high-resolution analysis of pine dispersal in New Zealand mountains
P Caplat, S Coutts, YM Buckley
Proceedings of the 17th Australian Weeds Conference (ed. SM Zydenbos), 190-193, 2010
A stochastic computer simulation of island group colonisation by Rattus norvegicus in small near shore island systems: specifically Tia Island and the Boat group
SR Coutts
University of Otago, 2005
Deep learning for robotic strawberry harvesting
X Li, C Fox, S Coutts
UK-RAS, 2020
Data from: Less favorable climates constrain demographic strategies in plants
AM Csergo, R Salguero-Gómez, O Broennimann, SR Coutts, A Guisan, ...
Scholars Portal Dataverse, 2021
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