Conducting robust ecological analyses with climate data

Although the number of studies discerning the impact of climate change on ecological systems continues to increase, there has been relatively little sharing of the lessons learnt when accumulating this evidence. At a recent workshop entitled ‘Using climate data in ecological research’ held at the UK Met Office, ecologists and climate scientists came together to discuss the robust analysis of climate data in ecology. The discussions identified three common pitfalls encountered by ecologists: 1) selection of inappropriate spatial resolutions for analysis; 2) improper use of publically available data or code; and 3) insufficient representation of the uncertainties behind the adopted approach. Here, we discuss how these pitfalls can be avoided, before suggesting ways that both ecology and climate science can move forward. Our main recommendation is that ecologists and climate scientists collaborate more closely, on grant proposals and scientific publications, and informally through online media and workshops. More sharing of data and code (e.g. via online repositories), lessons and guidance would help to reconcile differing approaches to the robust handling of data. We call on ecologists to think critically about which aspects of the climate are relevant to their study system, and to acknowledge and actively explore uncertainty in all types of climate data. And we call on climate scientists to make simple estimates of uncertainty available to the wider research community. Through steps such as these, we will improve our ability to robustly attribute observed ecological changes to climate or other factors, while providing the sort of influential, comprehensive analyses that efforts to mitigate and adapt to climate change so urgently require.

[1]  R. A. Garcia,et al.  Multiple Dimensions of Climate Change and Their Implications for Biodiversity , 2014, Science.

[2]  Robert J. Wilson,et al.  Topographic microclimates drive microhabitat associations at the range margin of a butterfly , 2014 .

[3]  Brett R. Scheffers,et al.  The broad footprint of climate change from genes to biomes to people , 2016, Science.

[4]  C. Thomas,et al.  Changing habitat associations of a thermally constrained species, the silver-spotted skipper butterfly, in response to climate warming. , 2006, The Journal of animal ecology.

[5]  N. Pettorelli,et al.  Satellite remote sensing for applied ecologists: opportunities and challenges , 2014 .

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

[7]  S. Pincebourde,et al.  Microclimatic challenges in global change biology , 2013, Global change biology.

[8]  Martijn van de Pol,et al.  Identifying the best climatic predictors in ecology and evolution , 2016 .

[9]  T. D. Mitchell,et al.  A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: the observed record (1901-2000) and 16 scenarios (2001-2100). , 2004 .

[10]  C. Baker Some problems in using meteorological data to forecast the timing of insect life cycles. , 1980 .

[11]  Chiara Polce,et al.  Predicting ground temperatures across European landscapes , 2015 .

[12]  R. Green,et al.  Birds and Climate Change: Birds and climate change , 2014 .

[13]  B. Huntley,et al.  Habitat microclimates drive fine‐scale variation in extreme temperatures , 2011 .

[14]  C. Daly,et al.  A knowledge-based approach to the statistical mapping of climate , 2002 .

[15]  M. Ashcroft Identifying refugia from climate change , 2010 .

[16]  Damien A. Fordham,et al.  Life history and spatial traits predict extinction risk due to climate change , 2014 .

[17]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[18]  Tim J. Hewison,et al.  The 30 year TAMSAT African Rainfall Climatology And Time series (TARCAT) data set , 2014 .

[19]  P. Bannister,et al.  Will loss of snow cover during climatic warming expose New Zealand alpine plants to increased frost damage? , 2005, Oecologia.

[20]  G. Huffman,et al.  Integrated Multi-satellitE Retrievals for GPM (IMERG) Technical Documentation , 2015 .

[21]  J. Michaelsen,et al.  The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes , 2015, Scientific Data.

[22]  Richard Fox,et al.  Temperature-Dependent Alterations in Host Use Drive Rapid Range Expansion in a Butterfly , 2012, Science.

[23]  Veronika Eyring,et al.  Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization , 2015 .

[24]  Gabriel Huerta,et al.  Uncertainty Quantification in Climate Modeling and Projection , 2016 .

[25]  D. Gutiérrez,et al.  Effects of temperature and elevation on habitat use by a rare mountain butterfly: implications for species responses to climate change , 2009 .

[26]  Ed Hawkins,et al.  Increasing influence of heat stress on French maize yields from the 1960s to the 2030s , 2012, Global change biology.

[27]  C. Beierkuhnlein,et al.  A new generation of climate‐change experiments: events, not trends , 2007 .

[28]  M. A N D A,et al.  Spatial scale affects bioclimate model projections of climate change impacts on mountain plants , 2008 .

[29]  C. Kummerow,et al.  The Tropical Rainfall Measuring Mission (TRMM) Sensor Package , 1998 .

[30]  Liam D. Bailey,et al.  Tackling extremes: challenges for ecological and evolutionary research on extreme climatic events. , 2016, The Journal of animal ecology.

[31]  Russell S. Vose,et al.  The Definition of the Standard WMO Climate Normal: The Key to Deriving Alternative Climate Normals , 2011 .

[32]  Suraje Dessai,et al.  Robust adaptation to climate change , 2010 .

[33]  Rebecca M. B. Harris,et al.  Improving the Use of Species Distribution Models in Conservation Planning and Management under Climate Change , 2014, PloS one.

[34]  John R. Gollan,et al.  Fine‐resolution (25 m) topoclimatic grids of near‐surface (5 cm) extreme temperatures and humidities across various habitats in a large (200 × 300 km) and diverse region , 2011 .

[35]  Kenton O'Hara,et al.  Troubling Trends in Scientific Software Use , 2013, Science.

[36]  B. Young,et al.  IUCN SSC guidelines for assessing species' vulnerability to climate change , 2016 .

[37]  M. Morecroft,et al.  Effects of drought on contrasting insect and plant species in the UK in the mid-1990s , 2002 .

[38]  Bruce L. Webber,et al.  CliMond: global high‐resolution historical and future scenario climate surfaces for bioclimatic modelling , 2012 .

[39]  Tom A. August,et al.  Climate change refugia for the flora and fauna of England , 2014 .

[40]  R. Warren,et al.  Sensitivity of UK butterflies to local climatic extremes: which life stages are most at risk? , 2017, The Journal of animal ecology.

[41]  C. Field,et al.  The velocity of climate change , 2009, Nature.

[42]  W. Kunin,et al.  The effect of spatial resolution on projected responses to climate warming , 2012 .

[43]  E. Meineri,et al.  Fine‐grain, large‐domain climate models based on climate station and comprehensive topographic information improve microrefugia detection , 2017 .

[44]  C. Körner,et al.  Topographically controlled thermal‐habitat differentiation buffers alpine plant diversity against climate warming , 2011 .

[45]  Peter Cornillon,et al.  The Past, Present, and Future of the AVHRR Pathfinder SST Program , 2010 .

[46]  R. Almond,et al.  Mechanisms underpinning climatic impacts on natural populations: altered species interactions are more important than direct effects , 2014, Global change biology.

[47]  S. Butchart,et al.  Choice of baseline climate data impacts projected species' responses to climate change , 2016, Global change biology.

[48]  A. Pitman,et al.  Why is the choice of future climate scenarios for species distribution modelling important? , 2008, Ecology letters.

[49]  S. Dobrowski A climatic basis for microrefugia: the influence of terrain on climate , 2011 .

[50]  Dieter Gerten,et al.  Impact of a Statistical Bias Correction on the Projected Hydrological Changes Obtained from Three GCMs and Two Hydrology Models , 2011 .

[51]  M. C. Urban,et al.  Coarse climate change projections for species living in a fine‐scaled world , 2017, Global change biology.

[52]  Robert J. Wilson,et al.  Seeing the woods for the trees – when is microclimate important in species distribution models? , 2014, Global change biology.

[53]  J. Lawton Are there general laws in ecology , 1999 .

[54]  M. Hill,et al.  Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland , 2008 .

[55]  Camilo Mora,et al.  The projected timing of climate departure from recent variability , 2013, Nature.

[56]  B. Huntley,et al.  Climatic Disequilibrium Threatens Conservation Priority Forests , 2018 .

[57]  Rory P. Wilson,et al.  Trends and perspectives in animal‐attached remote sensing , 2005 .

[58]  J. Duffy,et al.  Fine‐scale climate change: modelling spatial variation in biologically meaningful rates of warming , 2017, Global change biology.

[59]  T. Brereton,et al.  Range expansion through fragmented landscapes under a variable climate , 2013, Ecology letters.

[60]  M. Kearney,et al.  microclim: Global estimates of hourly microclimate based on long-term monthly climate averages , 2014, Scientific Data.

[61]  Darrel C. Ince,et al.  The case for open computer programs , 2012, Nature.

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

[63]  P. Marra,et al.  A blind spot in climate change vulnerability assessments , 2013 .

[64]  N. Zimmermann,et al.  Climatologies at high resolution for the earth’s land surface areas , 2016, Scientific Data.

[65]  M. Hulme,et al.  A high-resolution data set of surface climate over global land areas , 2002 .

[66]  M. C. Urban Accelerating extinction risk from climate change , 2015, Science.

[67]  Christian Körner,et al.  Infra‐red thermometry of alpine landscapes challenges climatic warming projections , 2009 .

[68]  J. Bartholy,et al.  Bridging the gap between climate models and impact studies: the FORESEE Database , 2015, Geoscience data journal.

[69]  Eric A. Rosenberg,et al.  A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions* , 2002 .

[70]  P. Platts,et al.  AFRICLIM: high‐resolution climate projections for ecological applications in Africa , 2015 .

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

[72]  M. Kearney,et al.  Activity restriction and the mechanistic basis for extinctions under climate warming. , 2013, Ecology letters.

[73]  C. Dytham,et al.  Climate change, climatic variation and extreme biological responses , 2017, Philosophical Transactions of the Royal Society B: Biological Sciences.

[74]  Miroslav Dudík,et al.  Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation , 2008 .

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

[76]  I. Martins,et al.  MODELLING THE EFFECTS OF GLOBAL TEMPERATURE INCREASE ON THE GROWTH OF SALT MARSH PLANTS , 2014 .