Modelling butterfly species richness using mesoscale environmental variables: Model construction and validation for mountain ranges in the Great Basin of western North America

R. Mac Nally, Erica Fleishman, J.P. Fay, Danielle Murphy

    Research output: Contribution to journalArticle

    31 Citations (Scopus)

    Abstract

    If species richness can be modelled as a function of easily quantified environmental variables, the scientific foundation for land-use planning will be strengthened. We used Poisson regression to develop a predictive model of species richness of resident butterflies in the central Great Basin of western North America. Species inventory data and values for 14 environmental variables from 49 locations (canyon segments) in the Toquima Range (Nevada, USA) were used to build the model. We also included squares of the environmental variables to accommodate potential non-linear relationships. Species richness of butterflies was a significant function of elevation and local topographic heterogeneity, with the selected model explaining 57% of the total deviance of species richness. Predictive variables can be derived efficiently from GIS-based data for areas in which species inventories have not yet been conducted. Therefore, in addition to evaluating the ability of the model to explain observed variation in species richness, we generated and tested predictions of species richness for ‘new’ locations that had not been used to build the model. Predictions were effective for five new segments also located in the Toquima Range, but not for 22 new segments in the nearby Shoshone Range. We discuss issues related to generalizability and data quality in model development.
    Original languageEnglish
    Pages (from-to)21-31
    Number of pages11
    JournalBiological Conservation
    Volume110
    Issue number1
    DOIs
    Publication statusPublished - 2003

    Fingerprint

    butterfly
    butterflies
    species richness
    mountains
    basins
    species diversity
    environmental factors
    basin
    species inventory
    modeling
    land use planning
    prediction
    canyons
    data quality
    canyon
    mountain range
    North America
    GIS

    Cite this

    @article{f3c0aae9ed64468db2c6feac8cf99b36,
    title = "Modelling butterfly species richness using mesoscale environmental variables: Model construction and validation for mountain ranges in the Great Basin of western North America",
    abstract = "If species richness can be modelled as a function of easily quantified environmental variables, the scientific foundation for land-use planning will be strengthened. We used Poisson regression to develop a predictive model of species richness of resident butterflies in the central Great Basin of western North America. Species inventory data and values for 14 environmental variables from 49 locations (canyon segments) in the Toquima Range (Nevada, USA) were used to build the model. We also included squares of the environmental variables to accommodate potential non-linear relationships. Species richness of butterflies was a significant function of elevation and local topographic heterogeneity, with the selected model explaining 57{\%} of the total deviance of species richness. Predictive variables can be derived efficiently from GIS-based data for areas in which species inventories have not yet been conducted. Therefore, in addition to evaluating the ability of the model to explain observed variation in species richness, we generated and tested predictions of species richness for ‘new’ locations that had not been used to build the model. Predictions were effective for five new segments also located in the Toquima Range, but not for 22 new segments in the nearby Shoshone Range. We discuss issues related to generalizability and data quality in model development.",
    author = "{Mac Nally}, R. and Erica Fleishman and J.P. Fay and Danielle Murphy",
    note = "Cited By :25 Export Date: 6 June 2017",
    year = "2003",
    doi = "10.1016/S0006-3207(02)00172-6",
    language = "English",
    volume = "110",
    pages = "21--31",
    journal = "Biological Conservation",
    issn = "0006-3207",
    publisher = "Elsevier BV",
    number = "1",

    }

    Modelling butterfly species richness using mesoscale environmental variables: Model construction and validation for mountain ranges in the Great Basin of western North America. / Mac Nally, R.; Fleishman, Erica; Fay, J.P.; Murphy, Danielle.

    In: Biological Conservation, Vol. 110, No. 1, 2003, p. 21-31.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - Modelling butterfly species richness using mesoscale environmental variables: Model construction and validation for mountain ranges in the Great Basin of western North America

    AU - Mac Nally, R.

    AU - Fleishman, Erica

    AU - Fay, J.P.

    AU - Murphy, Danielle

    N1 - Cited By :25 Export Date: 6 June 2017

    PY - 2003

    Y1 - 2003

    N2 - If species richness can be modelled as a function of easily quantified environmental variables, the scientific foundation for land-use planning will be strengthened. We used Poisson regression to develop a predictive model of species richness of resident butterflies in the central Great Basin of western North America. Species inventory data and values for 14 environmental variables from 49 locations (canyon segments) in the Toquima Range (Nevada, USA) were used to build the model. We also included squares of the environmental variables to accommodate potential non-linear relationships. Species richness of butterflies was a significant function of elevation and local topographic heterogeneity, with the selected model explaining 57% of the total deviance of species richness. Predictive variables can be derived efficiently from GIS-based data for areas in which species inventories have not yet been conducted. Therefore, in addition to evaluating the ability of the model to explain observed variation in species richness, we generated and tested predictions of species richness for ‘new’ locations that had not been used to build the model. Predictions were effective for five new segments also located in the Toquima Range, but not for 22 new segments in the nearby Shoshone Range. We discuss issues related to generalizability and data quality in model development.

    AB - If species richness can be modelled as a function of easily quantified environmental variables, the scientific foundation for land-use planning will be strengthened. We used Poisson regression to develop a predictive model of species richness of resident butterflies in the central Great Basin of western North America. Species inventory data and values for 14 environmental variables from 49 locations (canyon segments) in the Toquima Range (Nevada, USA) were used to build the model. We also included squares of the environmental variables to accommodate potential non-linear relationships. Species richness of butterflies was a significant function of elevation and local topographic heterogeneity, with the selected model explaining 57% of the total deviance of species richness. Predictive variables can be derived efficiently from GIS-based data for areas in which species inventories have not yet been conducted. Therefore, in addition to evaluating the ability of the model to explain observed variation in species richness, we generated and tested predictions of species richness for ‘new’ locations that had not been used to build the model. Predictions were effective for five new segments also located in the Toquima Range, but not for 22 new segments in the nearby Shoshone Range. We discuss issues related to generalizability and data quality in model development.

    U2 - 10.1016/S0006-3207(02)00172-6

    DO - 10.1016/S0006-3207(02)00172-6

    M3 - Article

    VL - 110

    SP - 21

    EP - 31

    JO - Biological Conservation

    JF - Biological Conservation

    SN - 0006-3207

    IS - 1

    ER -