The influence of data source and species distribution modelling method on spatial conservation priorities

Species distribution models are an important conservation tool; however, performance can vary with factors including data inputs and modelling method. Model outputs are often under‐evaluated for explanatory and predictive capacity. Our aim was to evaluate the capacity of existing data for seven small mammal species to provide useful inferences for management planning.

[1]  Simon Ferrier,et al.  Evaluating the predictive performance of habitat models developed using logistic regression , 2000 .

[2]  Carsten F. Dormann,et al.  Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure , 2017 .

[3]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[4]  Eric R. Ziegel,et al.  Generalized Linear Models , 2002, Technometrics.

[5]  Jane Elith,et al.  Not all data are equal: Influence of data type and amount in spatial conservation prioritisation , 2018, Methods in Ecology and Evolution.

[6]  J. Woinarski,et al.  Distributional patterning of mammals on the Wessel and English Company Islands, Arnhem Land, Northern Territory, Australia , 1999 .

[7]  David Whitehead,et al.  Soil and atmospheric water deficits and the distribution of New Zealand's indigenous tree species , 2001 .

[8]  Jane Elith,et al.  What do we gain from simplicity versus complexity in species distribution models , 2014 .

[9]  M. Sykes,et al.  Methods and uncertainties in bioclimatic envelope modelling under climate change , 2006 .

[10]  M. McCarthy,et al.  Declining populations in one of the last refuges for threatened mammal species in northern Australia , 2018 .

[11]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[12]  David R. B. Stockwell,et al.  Effects of sample size on accuracy of species distribution models , 2002 .

[13]  Hugh P Possingham,et al.  Delaying conservation actions for improved knowledge: how long should we wait? , 2009, Ecology letters.

[14]  Brendan A. Wintle,et al.  Is my species distribution model fit for purpose? Matching data and models to applications , 2015 .

[15]  P. Harrison,et al.  Ongoing unraveling of a continental fauna: Decline and extinction of Australian mammals since European settlement , 2015, Proceedings of the National Academy of Sciences.

[16]  C. Hempel,et al.  Environmental relationships of the brush‐tailed rabbit‐rat, Conilurus penicillatus, and other small mammals on the Tiwi Islands, northern Australia , 2006 .

[17]  J. Elith,et al.  Sensitivity of conservation planning to different approaches to using predicted species distribution data , 2005 .

[18]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[19]  Brendan A. Wintle,et al.  Evaluating 318 continental-scale species distribution models over a 60-year prediction horizon: what factors influence the reliability of predictions? , 2017 .

[20]  H. Messel,et al.  Surveys of tidal river systems in the Northern Territory of Australia and their crocodile populations , 1970 .

[21]  R. M. Nally Regression and model-building in conservation biology, biogeography and ecology: The distinction between – and reconciliation of – ‘predictive’ and ‘explanatory’ models , 2000, Biodiversity & Conservation.

[22]  Hugh P. Possingham,et al.  Diminishing return on investment for biodiversity data in conservation planning , 2008 .

[23]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[24]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[25]  Brendan A. Wintle,et al.  Predicting species distributions for conservation decisions , 2013, Ecology letters.

[26]  Hugh P. Possingham,et al.  How can you conserve species that haven't been found? , 2007 .

[27]  Matthew E. Watts,et al.  Marxan and relatives: Software for spatial conservation prioritization , 2009 .

[28]  Robert C. Bailey,et al.  Management options for river conservation planning: condition and conservation re‐visited , 2007 .

[29]  A. Griffiths,et al.  Activity area and day-time tree use of the black-footed tree-rat Mesembriomys gouldii. , 2001 .

[30]  G. Friend Population Ecology of Mesembriomys gouldii (Rodentia : Muridae) in the Wet-Dry Tropics of the Northern Territory , 1987 .

[31]  G. Gordon MOVEMENTS AND ACTIVITY OF THE SHORTNOSED BANDICOOT ISOODON MACROURUS GOULD (MARSUPIALIA) , 1974 .

[32]  Jennifer A. Miller,et al.  Mapping Species Distributions: Spatial Inference and Prediction , 2010 .

[33]  F. Courchamp,et al.  Predicting species distribution combining multi-scale drivers , 2017 .

[34]  S. King,et al.  Ecology of Melomys burtoni, the Grasslnad Melomys (Rodentia : Muridae) at Cobourg Peninsula, N.T , 1983 .

[35]  Richard Fox,et al.  Direct and indirect effects of climate and habitat factors on butterfly diversity. , 2007, Ecology.

[36]  Jane Elith,et al.  Error and uncertainty in habitat models , 2006 .

[37]  S. Ferrier Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? , 2002, Systematic biology.

[38]  Steven J. Phillips,et al.  Aligning Conservation Priorities Across Taxa in Madagascar with High-Resolution Planning Tools , 2008, Science.

[39]  Helen M. Regan,et al.  Mapping epistemic uncertainties and vague concepts in predictions of species distribution , 2002 .

[40]  A. Andersen,et al.  Savanna burning, greenhouse gas emissions and indigenous livelihoods: Introducing the Tiwi Carbon Study , 2012 .

[41]  F. Visser,et al.  Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry , 2015 .

[42]  Matthew C. Fitzpatrick,et al.  Field‐measured variables outperform derived alternatives in Maryland stream biodiversity models , 2017 .

[43]  S. Hooker,et al.  Ranging behaviour of forest-dwelling ship rats, Rattus rattus, and effects of poisoning with brodifacoum. , 1995 .

[44]  M. Austin Spatial prediction of species distribution: an interface between ecological theory and statistical modelling , 2002 .

[45]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[46]  A. Griffiths,et al.  The Paradox of Rattus Tunneyi: Endangerment of a Native Pest. , 1996 .

[47]  R. Tingley,et al.  Conservation planners tend to ignore improved accuracy of modelled species distributions to focus on multiple threats and ecological processes , 2016 .

[48]  I. Hollingsworth Land Capability Study of the Tiwi Islands , 2003 .

[49]  I. Abbott,et al.  Mammals of Australian islands: factors influencing species richness , 2003 .

[50]  N. Gotelli Predicting Species Occurrences: Issues of Accuracy and Scale , 2003 .

[51]  Trevor Hastie,et al.  Making better biogeographical predictions of species’ distributions , 2006 .

[52]  The distribution of the New Holland mouse (Pseudomys novaehollandiae) with respect to vegetation near Anglesea, Victoria , 1999 .

[53]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[54]  M. McCarthy,et al.  Top‐down control of species distributions: feral cats driving the regional extinction of a threatened rodent in northern Australia , 2017 .

[55]  J. Kerle THE POPULATION DYNAMICS OF A TROPICAL POSSUM, TRICHOSURUS VULPECULA ARNHEMENSIS COLLETT , 1998 .

[56]  Trevor Hastie,et al.  Novel methods for the design and evaluation of marine protected areas in offshore waters , 2008 .

[57]  John A. Taylor,et al.  Habitat preferences of small mammals in tropical open‐forest of the Northern Territory , 1985 .

[58]  J. Elith,et al.  Determinants of reproductive success in dominant pairs of clownfish: a boosted regression tree analysis. , 2011, The Journal of animal ecology.

[59]  Jian D. L. Yen,et al.  Model selection using information criteria, but is the "best" model any good? , 2018 .

[60]  Bryan F. J. Manly,et al.  Mammal extinctions on Australian islands: causes and conservation implications , 2002 .