Sensitivity analysis of the tree distribution model Phenofit to climatic input characteristics: implications for climate impact assessment

Species distributions are already affected by climate change. Forecasting their long-term evolution requires models with thoroughly assessed validation. Our aim here is to demonstrate that the sensitivity of such models to climate input characteristics may complicate their validation and introduce uncertainties in their predictions. In this study, we conducted a sensitivity analysis of a process-based tree distribution model PHENOFIT to climate input characteristics. This analysis was conducted for two North American trees which differ greatly in their distribution and eight different types of climate input for the historic period which differ in their spatial (local or gridded data) and temporal (daily vs. monthly) resolution as well as their type (locally recorded, extrapolated or simulated by General Circulation Models). We show that the climate data resolution (spatial and temporal) and their type, highly affect the model predictions. The sensitivity analysis also revealed, the importance, for global climate change impact assessment, of (i) the daily variability of temperatures in modeling the biological processes shaping species distribution, (ii) climate data at high latitudes and elevations and (iii) climate data with high spatial resolution.

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

[2]  Isabelle Chuine,et al.  Phenology is a major determinant of tree species range , 2001 .

[3]  W. Thuiller BIOMOD – optimizing predictions of species distributions and projecting potential future shifts under global change , 2003 .

[4]  S. Lavorel,et al.  Effects of restricting environmental range of data to project current and future species distributions , 2004 .

[5]  R L Keeney,et al.  A Framework to Guide Thinking and Analysis Regarding Climate Change Policies , 2001, Risk analysis : an official publication of the Society for Risk Analysis.

[6]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[7]  N Oreskes,et al.  Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences , 1994, Science.

[8]  S. Lavorel,et al.  Generalized models vs. classification tree analysis: Predicting spatial distributions of plant species at different scales , 2003 .

[9]  T. Dawson,et al.  Modelling potential impacts of climate change on the bioclimatic envelope of species in Britain and Ireland , 2002 .

[10]  K. Wessels,et al.  Vulnerability of South African animal taxa to climate change , 2002 .

[11]  Harold A. Mooney,et al.  A global distribution of biodiversity inferred from climatic constraints: results from a process‐based modelling study , 2000 .

[12]  G. Yohe,et al.  A globally coherent fingerprint of climate change impacts across natural systems , 2003, Nature.

[13]  R. Leemans,et al.  Comparing global vegetation maps with the Kappa statistic , 1992 .

[14]  C. Priestley,et al.  On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters , 1972 .

[15]  G. Walther,et al.  "Fingerprints" of climate change : adapted behaviour and shifting species ranges , 2001 .

[16]  M. Budyko,et al.  Climate and life , 1975 .

[17]  W. B. Critchfield,et al.  Geographic distribution of the pines of the world , 1966 .

[18]  C. Tucker,et al.  Increased plant growth in the northern high latitudes from 1981 to 1991 , 1997, Nature.

[19]  Hughes,et al.  Biological consequences of global warming: is the signal already apparent? , 2000, Trends in ecology & evolution.

[20]  Craig Leohle,et al.  Evaluation of theories and calculation tools in ecology , 1983 .

[21]  M. Schlesinger,et al.  The economic geography of the impacts of climate change , 2002 .

[22]  F. Kienast,et al.  A simulated map of the potential natural forest vegetation of Switzerland , 1993 .

[23]  Jennifer Bracy Toward the Universal , 1997 .

[24]  O. Hoegh‐Guldberg,et al.  Ecological responses to recent climate change , 2002, Nature.

[25]  Mark W. Schwartz,et al.  Modeling potential future individual tree-species distributions in the eastern United States under a climate change scenario: a case study with Pinus virginiana , 1999 .

[26]  S. Schneider,et al.  Fingerprints of global warming on wild animals and plants , 2003, Nature.

[27]  Annette Menzel,et al.  Growing season extended in Europe , 1999, Nature.

[28]  A. Lehmann,et al.  Regression models for spatial prediction: their role for biodiversity and conservation , 2002, Biodiversity & Conservation.

[29]  N. Nicholls Increased Australian wheat yield due to recent climate trends , 1997, Nature.

[30]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[31]  Edward J. Rykiel,et al.  Testing ecological models: the meaning of validation , 1996 .

[32]  M. Austin A silent clash of paradigms : some inconsistencies in community ecology , 1999 .

[33]  E. L. Little Atlas of United States trees. , 1971 .

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

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

[36]  David R. B. Stockwell,et al.  Future projections for Mexican faunas under global climate change scenarios , 2002, Nature.

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

[38]  G. Walther,et al.  “Fingerprints” of Climate Change , 2001 .

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

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

[41]  W. Thuiller Patterns and uncertainties of species' range shifts under climate change , 2004 .

[42]  T. E. Thórhallsdóttir Flowering phenology in the central highland of Iceland and implications for climatic warming in the Arctic , 1998, Oecologia.

[43]  Robert S. Thompson,et al.  Potential Changes in the Distributions of Western North America Tree and Shrub Taxa under Future Climate Scenarios , 2001, Ecosystems.

[44]  J. Guiot Statistical Analyses of Biospherical Variability , 1994 .

[45]  P. Jones,et al.  REPRESENTING TWENTIETH CENTURY SPACE-TIME CLIMATE VARIABILITY. , 1998 .

[46]  N. Stephenson Climatic Control of Vegetation Distribution: The Role of the Water Balance , 1990, The American Naturalist.

[47]  Richard Aspinall,et al.  Use of logistic regression for validation of maps of the spatial distribution of vegetation species derived from high spatial resolution hyperspectral remotely sensed data , 2002 .

[48]  R. Leemans,et al.  Assessing effects of forecasted climate change on the diversity and distribution of European higher plants for 2050 , 2002 .

[49]  Susan E. Lee,et al.  Contrasting physiological and structural vegetation feedbacks in climate change simulations , 1997, Nature.

[50]  W. Cramer,et al.  A global biome model based on plant physiology and dominance, soil properties and climate , 1992 .

[51]  John Pastor,et al.  Response of northern forests to CO2-induced climate change , 1988, Nature.

[52]  P. Michaelis On the economics of greenhouse gas accumulation: A simulation approach , 1994 .

[53]  L. Holdridge Determination of World Plant Formations From Simple Climatic Data. , 1947, Science.

[54]  W. Cramer,et al.  Special Paper: Modelling Present and Potential Future Ranges of Some European Higher Plants Using Climate Response Surfaces , 1995 .

[55]  C. Peng,et al.  Changes in Forest Biomass Carbon Storage in China Between 1949 and 1998 , 2001, Science.

[56]  Mike P. Austin,et al.  Continuum Concept, Ordination Methods, and Niche Theory , 1985 .

[57]  M. Claussen,et al.  Mid-Holocene greening of the Sahara: first results of the GAIM 6000 year BP Experiment with two asynchronously coupled atmosphere/biome models , 2000 .

[58]  C. D. Keeling,et al.  Increased activity of northern vegetation inferred from atmospheric CO2 measurements , 1996, Nature.

[59]  W. Cramer,et al.  The possible dynamic response of northern forests to global warming , 1991 .

[60]  A. Friend,et al.  UK conifer forests may be growing faster in response to increased N deposition, atmospheric CO2 and temperature , 1998 .

[61]  Wolfgang Cramer,et al.  A simulation model for the transient effects of climate change on forest landscapes , 1993 .

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

[63]  R. Neilson,et al.  Sensitivity of a biogeography model to soil properties , 1998 .

[64]  George Marsaglia,et al.  Toward a universal random number generator , 1987 .

[65]  C. Parmesan,et al.  Poleward shifts in geographical ranges of butterfly species associated with regional warming , 1999, Nature.

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

[67]  B. B. Stout,et al.  Atlas of the United States trees. V.1: Conifers and important hardwoods , 1972 .

[68]  C. W. Thornthwaite An approach toward a rational classification of climate. , 1948 .

[69]  R. Neilson A Model for Predicting Continental‐Scale Vegetation Distribution and Water Balance , 1995 .

[70]  S. Dullinger,et al.  Modelling climate change‐driven treeline shifts: relative effects of temperature increase, dispersal and invasibility , 2004 .

[71]  R. Green,et al.  A New Method of Recording Arterial Blood Pressure. , 1947, Science.

[72]  David R. Anderson,et al.  Modeling Survival and Testing Biological Hypotheses Using Marked Animals: A Unified Approach with Case Studies , 1992 .

[73]  Elbert L. Little,et al.  Atlas of United States Trees, Vol. 3: Minor Western Hardwoods , 1977 .

[74]  L. Beaumont,et al.  Potential changes in the distributions of latitudinally restricted Australian butterfly species in response to climate change , 2002 .