Interactive spatial scale effects on species distribution modeling: The case of the giant panda

Research has shown that varying spatial scale through the selection of the total extent of investigation and the grain size of environmental predictor variables has effects on species distribution model (SDM) results and accuracy, but there has been minimal investigation into the interactive effects of extent and grain. To do this, we used a consistently sampled range-wide dataset of giant panda occurrence across southwest China and modeled their habitat and distribution at 4 extents and 7 grain sizes. We found that increasing grain size reduced model accuracy at the smallest extent, but that increasing extent negated this effect. Increasing extent also generally increased model accuracy, but the models built at the second-largest (mountain range) extent were more accurate than those built at the largest, geographic range-wide extent. When predicting habitat suitability in the smallest nested extents (50 km2), we found that the models built at the next-largest extent (500 km2) were more accurate than the smallest-extent models but that further increases in extent resulted in large decreases in accuracy. Overall, this study highlights the impacts of the selection of spatial scale when evaluating species’ habitat and distributions, and we suggest more explicit investigations of scale effects in future modeling efforts.

[1]  Robert P. Anderson,et al.  The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela , 2010 .

[2]  Huanming Yang,et al.  Whole-genome sequencing of giant pandas provides insights into demographic history and local adaptation , 2012, Nature Genetics.

[3]  Douglas H. Johnson.  THE COMPARISON OF USAGE AND AVAILABILITY MEASUREMENTS FOR EVALUATING RESOURCE PREFERENCE , 1980 .

[4]  A. Viña,et al.  Uncertainty of future projections of species distributions in mountainous regions , 2018, PloS one.

[5]  Jianguo Liu,et al.  Telemetry research on elusive wildlife: A synthesis of studies on giant pandas. , 2016, Integrative zoology.

[6]  A. Viña,et al.  Mapping understory vegetation using phenological characteristics derived from remotely sensed data , 2010 .

[7]  William W. Taylor,et al.  A Framework for Evaluating the Effects of Human Factors on Wildlife Habitat: the Case of Giant Pandas , 1999 .

[8]  M. Austin,et al.  Improving species distribution models for climate change studies: variable selection and scale , 2011 .

[9]  Damaris Zurell,et al.  Collinearity: a review of methods to deal with it and a simulation study evaluating their performance , 2013 .

[10]  Michel Loreau,et al.  Connecting models, data, and concepts to understand fragmentation's ecosystem‐wide effects , 2017 .

[11]  Omri Allouche,et al.  Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS) , 2006 .

[12]  Ashton M. Shortridge,et al.  Effects of grain size and niche breadth on species distribution modeling , 2018 .

[13]  David R. Anderson,et al.  Multimodel Inference , 2004 .

[14]  M. Turner,et al.  LANDSCAPE ECOLOGY : The Effect of Pattern on Process 1 , 2002 .

[15]  Kevin McGarigal,et al.  The influence of research scale on bald eagle habitat selection along the lower Hudson River, New York (USA) , 2002, Landscape Ecology.

[16]  B. R. Ramesh,et al.  Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics , 2016 .

[17]  T. Dawson,et al.  Spatial scale affects bioclimate model projections of climate change impacts on mountain plants , 2008 .

[18]  Dan L Warren,et al.  Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. , 2011, Ecological applications : a publication of the Ecological Society of America.

[19]  W. Porter,et al.  An ecophysiological perspective on likely giant panda habitat responses to climate change , 2018, Global change biology.

[20]  Shanning Zhang,et al.  Old-growth forest is what giant pandas really need , 2011, Biology Letters.

[21]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[22]  L. Hannah,et al.  Scale effects in species distribution models: implications for conservation planning under climate change , 2009, Biology Letters.

[23]  H. Ernest,et al.  Multi-level, multi-scale resource selection functions and resistance surfaces for conservation planning: Pumas as a case study , 2017, PloS one.

[24]  S. Suárez‐Seoane,et al.  Non‐stationarity and local approaches to modelling the distributions of wildlife , 2007 .

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

[26]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[27]  R. Real,et al.  AUC: a misleading measure of the performance of predictive distribution models , 2008 .

[28]  E. Duffey.  The giant pandas of Wolong , 1986 .

[29]  Jianguo Liu,et al.  A synthesis of giant panda habitat selection , 2014 .

[30]  M. Hebblewhite,et al.  Transcending scale dependence in identifying habitat with resource selection functions. , 2012, Ecological applications : a publication of the Ecological Society of America.

[31]  R. DeFries,et al.  Range-wide analysis of wildlife habitat: Implications for conservation , 2010 .

[32]  S. Levin.  The problem of pattern and scale in ecology , 1992 .

[33]  D. Warton,et al.  Equivalence of MAXENT and Poisson Point Process Models for Species Distribution Modeling in Ecology , 2013, Biometrics.

[34]  Yibo Hu,et al.  Measures of giant panda habitat selection across multiple spatial scales for species conservation , 2012 .

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

[36]  Yoan Fourcade,et al.  Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics , 2018 .

[37]  J. Elith,et al.  Sensitivity of predictive species distribution models to change in grain size , 2007 .

[38]  Steven J. Phillips,et al.  WHAT MATTERS FOR PREDICTING THE OCCURRENCES OF TREES: TECHNIQUES, DATA, OR SPECIES' CHARACTERISTICS? , 2007 .

[39]  Steven J. Phillips,et al.  Point process models for presence‐only analysis , 2015 .

[40]  S. Levin.  THE PROBLEM OF PATTERN AND SCALE IN ECOLOGY , 1992 .

[41]  Samuel A. Cushman,et al.  Scale dependent inference in landscape genetics , 2010, Landscape Ecology.

[42]  M. Wheatley.  Domains of scale in forest-landscape metrics: Implications for species-habitat modeling , 2010 .

[43]  F. T. Manen.  Book Reivew: A chance for lasting survival: Ecology and behavior of wild giant pandas , 2015 .

[44]  Andrés Viña,et al.  Land surface phenology as an indicator of biodiversity patterns , 2016 .

[45]  J. Liu,et al.  Ecological Degradation in Protected Areas: The Case of Wolong Nature Reserve for Giant Pandas , 2001, Science.

[46]  Robert P. Anderson,et al.  Opening the black box: an open-source release of Maxent , 2017 .

[47]  Trevor Hastie,et al.  A statistical explanation of MaxEnt for ecologists , 2011 .

[48]  M. Lukac,et al.  Species distribution model transferability and model grain size – finer may not always be better , 2018, Scientific Reports.

[49]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[50]  A. Viña,et al.  Range-wide evaluation of wildlife habitat change: A demonstration using Giant Pandas , 2017 .

[51]  Christopher Daly,et al.  Guidelines for assessing the suitability of spatial climate data sets , 2006 .

[52]  Ashton M. Shortridge,et al.  Effects of payments for ecosystem services on wildlife habitat recovery , 2016, Conservation biology : the journal of the Society for Conservation Biology.

[53]  Steven J. Phillips,et al.  Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. , 2009, Ecological applications : a publication of the Ecological Society of America.

[54]  J. Wiens.  Spatial Scaling in Ecology , 1989 .

[55]  J. C. Brito,et al.  Predicting species distribution at range margins: testing the effects of study area extent, resolution and threshold selection in the Sahara–Sahel transition zone , 2014 .

[56]  H. Akaike.  Stochastic theory of minimal realization , 1974 .

[57]  P. Hernandez,et al.  The effect of sample size and species characteristics on performance of different species distribution modeling methods , 2006 .

[58]  A. Flint,et al.  Maxent modeling for predicting potential distribution of goitered gazelle in central Iran: the effect of extent and grain size on performance of the model , 2016 .

[59]  Matthew J. Smith,et al.  Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates , 2013, bioRxiv.

[60]  J. Chave.  The problem of pattern and scale in ecology: what have we learned in 20 years? , 2013, Ecology letters.

[61]  V. Saab.  IMPORTANCE OF SPATIAL SCALE TO HABITAT USE BY BREEDING BIRDS IN RIPARIAN FORESTS: A HIERARCHICAL ANALYSIS , 1999 .

[62]  Z. Ouyang,et al.  Role of New Nature Reserve in Assisting Endangered Species Conservation - Case Study of Giant Pandas in the Northern Qionglai Mountains, China , 2016, PloS one.

[63]  Jianguo Liu,et al.  Effects of fuelwood collection and timber harvesting on giant panda habitat use , 2008 .

[64]  Ashton Shortridge,et al.  Habitat Use and Selection by Giant Pandas , 2016, PloS one.