TY - JOUR
T1 - Understanding species distribution in dynamic populations: a new approach using spatio-temporal point process models
AU - Soriano-Redondo, Andrea
AU - Jones-Todd, Charlotte M.
AU - Bearhop, Stuart
AU - Hilton, Geoff M.
AU - Lock, Leigh
AU - Stanbury, Andrew
AU - Votier, Stephen C.
AU - Illian, Janine B.
N1 - Funding Information:
Funding – We are grateful to the Great Crane Project, a partnership between the Wildfowl and Wetlands Trust, the Royal Society for the Protection of Birds and Pensthorpe Conservation Trust, with major funding from Viridor Credits Environmental Company, for their financial and logistic support. SB is funded by an EU consolidator’s grant: STATEMIG 310820. ASR is supported by a joint predoctoral grant from the Univ. of Exeter, the Wildfowl and Wetlands Trust and the Royal Society for the Protection of Birds. Author contributions – ASR, CJT, SB, GMH, SCV and JBI designed the study, AS collected the data, ASR and CJT analysed the data and ASR drafted the paper, all authors contributed substantially to revising and preparing the paper; JBI supervised all stages of the research.
Publisher Copyright:
© 2019 The Authors
PY - 2019/6
Y1 - 2019/6
N2 - Understanding and predicting a species’ distribution across a landscape is of central importance in ecology, biogeography and conservation biology. However, it presents daunting challenges when populations are highly dynamic (i.e. increasing or decreasing their ranges), particularly for small populations where information about ecology and life history traits is lacking. Currently, many modelling approaches fail to distinguish whether a site is unoccupied because the available habitat is unsuitable or because a species expanding its range has not arrived at the site yet. As a result, habitat that is indeed suitable may appear unsuitable. To overcome some of these limitations, we use a statistical modelling approach based on spatio-temporal log-Gaussian Cox processes. These model the spatial distribution of the species across available habitat and how this distribution changes over time, relative to covariates. In addition, the model explicitly accounts for spatio-temporal dynamics that are unaccounted for by covariates through a spatio-temporal stochastic process. We illustrate the approach by predicting the distribution of a recently established population of Eurasian cranes Grus grus in England, UK, and estimate the effect of a reintroduction in the range expansion of the population. Our models show that wetland extent and perimeter-to-area ratio have a positive and negative effect, respectively, in crane colonisation probability. Moreover, we find that cranes are more likely to colonise areas near already occupied wetlands and that the colonisation process is progressing at a low rate. Finally, the reintroduction of cranes in SW England can be considered a human-assisted long-distance dispersal event that has increased the dispersal potential of the species along a longitudinal axis in S England. Spatio-temporal log-Gaussian Cox process models offer an excellent opportunity for the study of species where information on life history traits is lacking, since these are represented through the spatio-temporal dynamics reflected in the model.
AB - Understanding and predicting a species’ distribution across a landscape is of central importance in ecology, biogeography and conservation biology. However, it presents daunting challenges when populations are highly dynamic (i.e. increasing or decreasing their ranges), particularly for small populations where information about ecology and life history traits is lacking. Currently, many modelling approaches fail to distinguish whether a site is unoccupied because the available habitat is unsuitable or because a species expanding its range has not arrived at the site yet. As a result, habitat that is indeed suitable may appear unsuitable. To overcome some of these limitations, we use a statistical modelling approach based on spatio-temporal log-Gaussian Cox processes. These model the spatial distribution of the species across available habitat and how this distribution changes over time, relative to covariates. In addition, the model explicitly accounts for spatio-temporal dynamics that are unaccounted for by covariates through a spatio-temporal stochastic process. We illustrate the approach by predicting the distribution of a recently established population of Eurasian cranes Grus grus in England, UK, and estimate the effect of a reintroduction in the range expansion of the population. Our models show that wetland extent and perimeter-to-area ratio have a positive and negative effect, respectively, in crane colonisation probability. Moreover, we find that cranes are more likely to colonise areas near already occupied wetlands and that the colonisation process is progressing at a low rate. Finally, the reintroduction of cranes in SW England can be considered a human-assisted long-distance dispersal event that has increased the dispersal potential of the species along a longitudinal axis in S England. Spatio-temporal log-Gaussian Cox process models offer an excellent opportunity for the study of species where information on life history traits is lacking, since these are represented through the spatio-temporal dynamics reflected in the model.
KW - point process models
KW - spatio-temporal log-Gaussian Cox process
KW - species distribution models
UR - http://www.scopus.com/inward/record.url?scp=85062478695&partnerID=8YFLogxK
U2 - 10.1111/ecog.03771
DO - 10.1111/ecog.03771
M3 - Article
AN - SCOPUS:85062478695
SN - 0906-7590
VL - 42
SP - 1092
EP - 1102
JO - Ecography
JF - Ecography
IS - 6
ER -