Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar

Aim  Techniques that predict species potential distributions by combining observed occurrence records with environmental variables show much potential for application across a range of biogeographical analyses. Some of the most promising applications relate to species for which occurrence records are scarce, due to cryptic habits, locally restricted distributions or low sampling effort. However, the minimum sample sizes required to yield useful predictions remain difficult to determine. Here we developed and tested a novel jackknife validation approach to assess the ability to predict species occurrence when fewer than 25 occurrence records are available.

[1]  A. Peterson,et al.  The need for continued scientific collecting; a geographic analysis of Mexican bird specimens , 2008 .

[2]  F. Hartig,et al.  Biodiversity Conservation , 2020, Resource and Environmental Economics.

[3]  T. Dawson,et al.  Model‐based uncertainty in species range prediction , 2006 .

[4]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

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

[6]  Michael Hoffmann,et al.  Pinpointing and preventing imminent extinctions. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[7]  D. Richardson,et al.  Niche‐based modelling as a tool for predicting the risk of alien plant invasions at a global scale , 2005, Global change biology.

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

[9]  M. Luoto,et al.  Uncertainty of bioclimate envelope models based on the geographical distribution of species , 2005 .

[10]  William J. McShea,et al.  PUTTING A CART BEFORE THE SEARCH: SUCCESSFUL HABITAT PREDICTION FOR A RARE FOREST HERB , 2005 .

[11]  M. Araújo,et al.  Equilibrium of species’ distributions with climate , 2005 .

[12]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[13]  M. Araújo,et al.  Validation of species–climate impact models under climate change , 2005 .

[14]  M. Sykes,et al.  Climate change threats to plant diversity in Europe. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[15]  T. Dawson,et al.  Selecting thresholds of occurrence in the prediction of species distributions , 2005 .

[16]  J. Hoeting,et al.  FACTORS AFFECTING SPECIES DISTRIBUTION PREDICTIONS: A SIMULATION MODELING EXPERIMENT , 2005 .

[17]  A. Peterson,et al.  INTERPRETATION OF MODELS OF FUNDAMENTAL ECOLOGICAL NICHES AND SPECIES' DISTRIBUTIONAL AREAS , 2005 .

[18]  A. Peterson,et al.  Geographical potential of Argentine ants (Linepithema humile Mayr) in the face of global climate change , 2004, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[19]  M. Araújo,et al.  An evaluation of methods for modelling species distributions , 2004 .

[20]  A. Peterson,et al.  New developments in museum-based informatics and applications in biodiversity analysis. , 2004, Trends in ecology & evolution.

[21]  A. Hampe Bioclimate envelope models: what they detect and what they hide , 2004 .

[22]  C. Graham,et al.  INTEGRATING PHYLOGENETICS AND ENVIRONMENTAL NICHE MODELS TO EXPLORE SPECIATION MECHANISMS IN DENDROBATID FROGS , 2004, Evolution; international journal of organic evolution.

[23]  J. Svenning,et al.  Limited filling of the potential range in European tree species , 2004 .

[24]  E. Buffetaut,et al.  Pterosaurs as part of a spinosaur diet , 2004, Nature.

[25]  S. Lavorel,et al.  Biodiversity conservation: Uncertainty in predictions of extinction risk , 2004, Nature.

[26]  A. Peterson,et al.  Evolution of seasonal ecological niches in the Passerina buntings (Aves: Cardinalidae) , 2004, Proceedings of the Royal Society of London. Series B: Biological Sciences.

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

[28]  T. Dawson,et al.  Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data , 2004 .

[29]  A. Peterson,et al.  Biodiversity informatics: managing and applying primary biodiversity data. , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[30]  A. Guisan,et al.  An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data , 2004 .

[31]  Robert P. Anderson,et al.  Modeling species’ geographic distributions for preliminary conservation assessments: an implementation with the spiny pocket mice (Heteromys) of Ecuador , 2004 .

[32]  O. Phillips,et al.  Extinction risk from climate change , 2004, Nature.

[33]  A. Peterson,et al.  Modelling spatial patterns of biodiversity for conservation prioritization in North‐eastern Mexico , 2004 .

[34]  Michael Drielsma,et al.  Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling , 2002, Biodiversity & Conservation.

[35]  G. Carpenter,et al.  DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals , 1993, Biodiversity & Conservation.

[36]  S. Ferrier,et al.  Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modelling , 2004, Biodiversity & Conservation.

[37]  A. Peterson,et al.  Predicting distributions of known and unknown reptile species in Madagascar , 2003, Nature.

[38]  Bette A. Loiselle,et al.  Avoiding Pitfalls of Using Species Distribution Models in Conservation Planning , 2003 .

[39]  A. Peterson Predicting the Geography of Species’ Invasions via Ecological Niche Modeling , 2003, The Quarterly Review of Biology.

[40]  S. Reddy,et al.  Geographical sampling bias and its implications for conservation priorities in Africa , 2003 .

[41]  A. Peterson,et al.  Ecological niche differentiation in the Aphelocoma jays: a phylogenetic perspective , 2003 .

[42]  T. Dawson,et al.  Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? , 2003 .

[43]  A. Peterson,et al.  Lutzomyia vectors for cutaneous leishmaniasis in Southern Brazil: ecological niche models, predicted geographic distributions, and climate change effects. , 2003, International journal for parasitology.

[44]  S. Lavorel,et al.  Large-scale environmental correlates of forest tree distributions in Catalonia (NE Spain) , 2003 .

[45]  Ralph Mac Nally,et al.  Validation Tests of Predictive Models of Butterfly Occurrence Based on Environmental Variables , 2003 .

[46]  Robert P. Anderson,et al.  Evaluating predictive models of species’ distributions: criteria for selecting optimal models , 2003 .

[47]  M. Boyce,et al.  Evaluating resource selection functions , 2002 .

[48]  A. Lehmann,et al.  Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns , 2002 .

[49]  T. Dawson,et al.  SPECIES: A Spatial Evaluation of Climate Impact on the Envelope of Species , 2002 .

[50]  D. Chessel,et al.  ECOLOGICAL-NICHE FACTOR ANALYSIS: HOW TO COMPUTE HABITAT-SUITABILITY MAPS WITHOUT ABSENCE DATA? , 2002 .

[51]  Robert P. Anderson,et al.  Geographical distributions of spiny pocket mice in South America: insights from predictive models , 2002 .

[52]  C. Raxworthy,et al.  Chameleon radiation by oceanic dispersal , 2002, Nature.

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

[54]  Madhuri S. Mulekar,et al.  Statistical Inference in Science , 2001, Technometrics.

[55]  M. Vences,et al.  Patterns of amphibian and reptile diversity at Berara Forest (Sahamalaza Peninsula), NW Madagascar , 2001 .

[56]  Jason W. Karl,et al.  SENSITIVITY OF SPECIES HABITAT-RELATIONSHIP MODEL PERFORMANCE TO FACTORS OF SCALE , 2000 .

[57]  Miguel B. Araújo,et al.  Selecting areas for species persistence using occurrence data , 2000 .

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

[59]  R. Mittermeier,et al.  Biodiversity hotspots for conservation priorities , 2000, Nature.

[60]  V. Sánchez‐Cordero,et al.  Conservatism of ecological niches in evolutionary time , 1999, Science.

[61]  David R. B. Stockwell,et al.  The GARP modelling system: problems and solutions to automated spatial prediction , 1999, Int. J. Geogr. Inf. Sci..

[62]  A. Prasad,et al.  PREDICTING ABUNDANCE OF 80 TREE SPECIES FOLLOWING CLIMATE CHANGE IN THE EASTERN UNITED STATES , 1998 .

[63]  A. Herman,et al.  Objectively determined 10-day African rainfall estimates created for famine early warning systems , 1997 .

[64]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[65]  Phillip A. Arkin,et al.  Analyses of Global Monthly Precipitation Using Gauge Observations, Satellite Estimates, and Numerical Model Predictions , 1996 .

[66]  S. Kelley,et al.  Timing of Hot Spot—Related Volcanism and the Breakup of Madagascar and India , 1995, Science.

[67]  W. Böhme,et al.  Studien an Uroplatus. I, Der Uroplatus-fimbriatus-Komplex , 1990 .

[68]  A. Bauer,et al.  A systematic review of the genus Uroplatus (Reptilia: Gekkonidae), with comments on its biology , 1989 .

[69]  G. Donque The Climatology of Madagascar , 1972 .