Skip to content

Research at St Andrews

Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models

Research output: Contribution to journalArticle

DOI

Standard

Understanding species distribution in dynamic populations : a new approach using spatio‐temporal point process models. / Soriano-Redondo, Andrea; Jones-Todd, Charlotte M.; Bearhop, Stuart; Hilton, Geoff M.; Lock, Leigh; Stanbury, Andrew; Votier, Stephen C.; Illian, Janine B.

In: Ecography, Vol. Early View, 04.03.2019.

Research output: Contribution to journalArticle

Harvard

Soriano-Redondo, A, Jones-Todd, CM, Bearhop, S, Hilton, GM, Lock, L, Stanbury, A, Votier, SC & Illian, JB 2019, 'Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models', Ecography, vol. Early View. https://doi.org/10.1111/ecog.03771

APA

Soriano-Redondo, A., Jones-Todd, C. M., Bearhop, S., Hilton, G. M., Lock, L., Stanbury, A., ... Illian, J. B. (2019). Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models. Ecography, Early View. https://doi.org/10.1111/ecog.03771

Vancouver

Soriano-Redondo A, Jones-Todd CM, Bearhop S, Hilton GM, Lock L, Stanbury A et al. Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models. Ecography. 2019 Mar 4;Early View. https://doi.org/10.1111/ecog.03771

Author

Soriano-Redondo, Andrea ; Jones-Todd, Charlotte M. ; Bearhop, Stuart ; Hilton, Geoff M. ; Lock, Leigh ; Stanbury, Andrew ; Votier, Stephen C. ; Illian, Janine B. / Understanding species distribution in dynamic populations : a new approach using spatio‐temporal point process models. In: Ecography. 2019 ; Vol. Early View.

Bibtex - Download

@article{7ca29b30ff9d467ea7abe3deefeff651,
title = "Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models",
abstract = "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.",
keywords = "Point process models, Spatio-temporal log-Gaussian Cox process, Species distribution model",
author = "Andrea Soriano-Redondo and Jones-Todd, {Charlotte M.} and Stuart Bearhop and Hilton, {Geoff M.} and Leigh Lock and Andrew Stanbury and Votier, {Stephen C.} and Illian, {Janine B.}",
note = "Funding: EU consolidator’s grant STATEMIG 310820 (SB).",
year = "2019",
month = "3",
day = "4",
doi = "10.1111/ecog.03771",
language = "English",
volume = "Early View",
journal = "Ecography",
issn = "0906-7590",
publisher = "John Wiley & Sons, Ltd (10.1111)",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Understanding species distribution in dynamic populations

T2 - 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: EU consolidator’s grant STATEMIG 310820 (SB).

PY - 2019/3/4

Y1 - 2019/3/4

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 model

U2 - 10.1111/ecog.03771

DO - 10.1111/ecog.03771

M3 - Article

VL - Early View

JO - Ecography

JF - Ecography

SN - 0906-7590

ER -

Related by journal

  1. Ecography (Journal)

    Maria Dornelas (Member of editorial board)
    20142015

    Activity: Publication peer-review and editorial work typesEditor of research journal

Related by journal

  1. Assessing the effectiveness of foraging radius models for seabird distributions using biotelemetry and survey data

    Critchley, E. J., Grecian, W. J., Bennison, A., Kane, A., Wischnewski, S., Canadas, A., Tierney, D., Quinn, J. L. & Jessopp, M. J., 1 Nov 2019, In : Ecography. Early View, 13 p.

    Research output: Contribution to journalArticle

  2. β-diversity scaling patterns are consistent across metrics and taxa

    Antão, L. H., McGill, B., Magurran, A. E., Soares, A. & Dornelas, M., May 2019, In : Ecography. 42, 5, p. 1012-1023

    Research output: Contribution to journalArticle

  3. Peatland forests are the least diverse tree communities documented in Amazonia, but contribute to high regional beta-diversity

    Draper, F. C., Coronado, E. N. H., Roucoux, K. H., Lawson, I. T., Pitman, N. C. A., Fine, P. V. A., Phillips, O. L., Montenegro, L. A. T., Valderrama Sandoval, E., Mesones, I., García-Villacorta, R., Arévalo, F. R. R. & Baker, T. R., 16 Jan 2018, In : Ecography. Early View

    Research output: Contribution to journalArticle

  4. A recipe for scavenging in vertebrates - the natural history of a behaviour

    Kane, A., Healy, K., Guillerme, T., Ruxton, G. D. & Jackson, A. L., 1 Feb 2017, In : Ecography. 40, 2, p. 324-334 11 p.

    Research output: Contribution to journalReview article

ID: 257744431

Top