Terence Dawson

Dr. Terence Dawson


Research Focus & Metrics

Academic Career Length

29Years (1998-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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21 August 2003 in Global Ecology and Biogeography
Verified
Journal Article
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RPRichard G. Pearson
Richard G. Pearson
TDTerence Dawson
Terence Dawson

1 June 2005 in Ecography
Verified
Journal Article
Open access
PDF file
CLCanran Liu
Canran Liu
PBPam Berry
Pam Berry
TDTerence Dawson
Terence Dawson
RPRichard G. Pearson
Richard G. Pearson
Transforming the results of species distribution modelling from probabilities of or suitabilities for species occurrence to presences/absences needs a specific threshold. Even though there are many approaches to determining thresholds, there is no comparative study. In this paper, twelve approaches were compared using two species in Europe and artificial neural networks, and the modelling results were assessed using four indices: sensitivity, specificity, overall prediction success and Cohen's kappa statistic. The results show that prevalence approach, average predicted probability/suitability approach, and three sensitivity‐specificity‐combined approaches, including sensitivity‐specificity sum maximization approach, sensitivity‐specificity equality approach and the approach based on the shortest distance to the top‐left corner (0,1) in ROC plot, are the good ones. The commonly used kappa maximization approach is not as good as the afore‐mentioned ones, and the fixed threshold approach is the worst one. We also recommend using datasets with prevalence of 50% to build models if possible since most optimization criteria might be satisfied or nearly satisfied at the same time, and therefore it's easier to find optimal thresholds in this situation.

1 April 2011 in Science
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Journal Article
Closed access
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TDTerence Dawson
Terence Dawson
SJStephen T. Jackson
Stephen T. Jackson
JHJoanna I. House
Joanna I. House
IPIain Colin Prentice
Iain Colin Prentice
GMGeorgina M. Mace
Georgina M. Mace
Climate change is predicted to become a major threat to biodiversity in the 21st century, but accurate predictions and effective solutions have proved difficult to formulate. Alarming predictions have come from a rather narrow methodological base, but a new, integrated science of climate-change biodiversity assessment is emerging, based on multiple sources and approaches. Drawing on evidence from paleoecological observations, recent phenological and microevolutionary responses, experiments, and computational models, we review the insights that different approaches bring to anticipating and managing the biodiversity consequences of climate change, including the extent of species’ natural resilience. We introduce a framework that uses information from different sources to identify vulnerability and to support the design of conservation responses. Although much of the information reviewed is on species, our framework and conclusions are also applicable to ecosystems, habitats, ecological communities, and genetic diversity, whether terrestrial, marine, or fresh water.

13 April 2006 in Journal of Biogeography
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Journal Article
Closed access
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RPRichard G. Pearson
Richard G. Pearson
WTWilfried Thuiller
Wilfried Thuiller
MAMiguel B. Araújo
Miguel B. Araújo
EMEnrique Martinez‐Meyer
Enrique Martinez‐Meyer
LBLluís Brotons
Lluís Brotons
DLDavid C. Lees
David C. Lees

25 September 2013 in Nature
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Journal Article
Closed access
RSRick D. Stuart-Smith
Rick D. Stuart-Smith
ABAmanda E. Bates
Amanda E. Bates
Jonathan S. LefcheckJonathan S. Lefcheck
Jonathan S. Lefcheck
JDJ. Emmett Duffy
J. Emmett Duffy
SBSusan Baker
Susan Baker
GEGraham J. Edgar
Graham J. Edgar

14 May 2004 in Ecography
Verified
Journal Article
Closed access
PDF file
RPRichard G. Pearson
Richard G. Pearson
TDTerence Dawson
Terence Dawson
CLCanran Liu
Canran Liu