Modeling and predicting species occurrence using broad-scale environmental variables: An example with butterflies of the great basin

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

    Research output: Contribution to journalArticle

    108 Citations (Scopus)

    Abstract

    If occurrence of individual species can be modeled as a function of easily quantified environmental variables (e.g., derived from a geographic information system [GIS]) and the predictions of these models are demonstrably successful, then the scientific foundation for management planning will be strengthened. We used Bayesian logistic regression to develop predictive models for resident butterflies in the central Great Basin of western North America. Species inventory data and values for 14 environmental variables from 49 locations (segments of canyons) in the Toquima Range ( Nevada, U.S.A.) were used to build the models. Squares of the environmental variables were also used to accommodate possibly nonmonotonic responses. We obtained statistically significant models for 36 of 56 (64%) resident species of butterflies. The models explained 8–72% of the deviance in occurrence of those species. Each of the independent variables was significant in at least one model, and squared versions of five variables contributed to models. Elevation was included in more than half of the models. Models included one to four variables; only one variable was significant in about half the models. We conducted preliminary tests of two of our models by using an existing set of data on the occurrence of butterflies in the neighboring Toiyabe Range. We compared conventional logistic classification with posterior probability distributions derived from Bayesian modeling. For the latter, we restricted our predictions to locations with a high ( 70%) probability of predicted presence or absence. We will perform further tests after conducting inventories at new locations in the Toquima Range and nearby Shoshone Mountains, for which we have computed environmental variables by using remotely acquired topographic data, digital-terrain and microclimatic models, and GIS computation.
    Original languageEnglish
    Pages (from-to)1674-1685
    Number of pages12
    JournalConservation Biology
    Volume15
    Issue number6
    DOIs
    Publication statusPublished - 2001

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    species occurrence
    butterfly
    butterflies
    basins
    environmental factors
    basin
    modeling
    geographic information systems
    logistics
    species inventory
    prediction
    canyons
    probability distribution
    canyon
    planning

    Cite this

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    abstract = "If occurrence of individual species can be modeled as a function of easily quantified environmental variables (e.g., derived from a geographic information system [GIS]) and the predictions of these models are demonstrably successful, then the scientific foundation for management planning will be strengthened. We used Bayesian logistic regression to develop predictive models for resident butterflies in the central Great Basin of western North America. Species inventory data and values for 14 environmental variables from 49 locations (segments of canyons) in the Toquima Range ( Nevada, U.S.A.) were used to build the models. Squares of the environmental variables were also used to accommodate possibly nonmonotonic responses. We obtained statistically significant models for 36 of 56 (64{\%}) resident species of butterflies. The models explained 8–72{\%} of the deviance in occurrence of those species. Each of the independent variables was significant in at least one model, and squared versions of five variables contributed to models. Elevation was included in more than half of the models. Models included one to four variables; only one variable was significant in about half the models. We conducted preliminary tests of two of our models by using an existing set of data on the occurrence of butterflies in the neighboring Toiyabe Range. We compared conventional logistic classification with posterior probability distributions derived from Bayesian modeling. For the latter, we restricted our predictions to locations with a high ( 70{\%}) probability of predicted presence or absence. We will perform further tests after conducting inventories at new locations in the Toquima Range and nearby Shoshone Mountains, for which we have computed environmental variables by using remotely acquired topographic data, digital-terrain and microclimatic models, and GIS computation.",
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    Modeling and predicting species occurrence using broad-scale environmental variables: An example with butterflies of the great basin. / Fleishman, Erica; Mac Nally, R.; Fay, J.P.; Murphy, Danielle.

    In: Conservation Biology, Vol. 15, No. 6, 2001, p. 1674-1685.

    Research output: Contribution to journalArticle

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    AU - Fleishman, Erica

    AU - Mac Nally, R.

    AU - Fay, J.P.

    AU - Murphy, Danielle

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    PY - 2001

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