Less favourable climates constrain demographic strategies in plants

Abstract Correlative species distribution models are based on the observed relationship between species’ occurrence and macroclimate or other environmental variables. In climates predicted less favourable populations are expected to decline, and in favourable climates they are expected to persist. However, little comparative empirical support exists for a relationship between predicted climate suitability and population performance. We found that the performance of 93 populations of 34 plant species worldwide – as measured by in situ population growth rate, its temporal variation and extinction risk – was not correlated with climate suitability. However, correlations of demographic processes underpinning population performance with climate suitability indicated both resistance and vulnerability pathways of population responses to climate: in less suitable climates, plants experienced greater retrogression (resistance pathway) and greater variability in some demographic rates (vulnerability pathway). While a range of demographic strategies occur within species’ climatic niches, demographic strategies are more constrained in climates predicted to be less suitable.

[1]  R. Pradel,et al.  Climate‐driven vital rates do not always mean climate‐driven population , 2016, Global change biology.

[2]  Antoine Guisan,et al.  What we use is not what we know: environmental predictors in plant distribution models , 2016 .

[3]  Cory Merow,et al.  Towards Process-based Range Modeling of Many Species. , 2016, Trends in ecology & evolution.

[4]  Roberto Salguero-Gómez,et al.  Extrapolating demography with climate, proximity and phylogeny: approach with caution. , 2016, Ecology letters.

[5]  Heather M. Kharouba,et al.  A synthesis of transplant experiments and ecological niche models suggests that range limits are often niche limits. , 2016, Ecology letters.

[6]  K. Esler,et al.  Environmental drivers of demographic variation across the global geographical range of 26 plant species , 2016 .

[7]  P. Adler,et al.  Linking transient dynamics and life history to biological invasion success , 2016 .

[8]  Allison M. Louthan,et al.  Where and When do Species Interactions Set Range Limits? , 2015, Trends in ecology & evolution.

[9]  William F Morris,et al.  Demographic compensation among populations: what is it, how does it arise and what are its implications? , 2015, Ecology letters.

[10]  E. Nielsen,et al.  Arctic warming will promote Atlantic–Pacific fish interchange , 2015 .

[11]  Johan Ehrlén,et al.  Predicting changes in the distribution and abundance of species under environmental change , 2015, Ecology letters.

[12]  Ming Dong,et al.  The compadre Plant Matrix Database: an open online repository for plant demography , 2015 .

[13]  Damaris Zurell,et al.  Does probability of occurrence relate to population dynamics? , 2014, Ecography.

[14]  A. Nicotra,et al.  The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. , 2014, Ecology letters.

[15]  J. Ehrlén,et al.  Local environment and density-dependent feedbacks determine population growth in a forest herb , 2014, Oecologia.

[16]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[17]  Antoine Guisan,et al.  Biodiversity: Predictive traits to the rescue , 2014 .

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

[19]  Martha M. Ellis,et al.  Ability of Matrix Models to Explain the Past and Predict the Future of Plant Populations , 2013, Conservation biology : the journal of the Society for Conservation Biology.

[20]  J. Svenning,et al.  Disequilibrium vegetation dynamics under future climate change. , 2013, American journal of botany.

[21]  K. Hylander,et al.  The mechanisms causing extinction debts. , 2013, Trends in ecology & evolution.

[22]  J. Gibbs,et al.  Reexamining the Minimum Viable Population Concept for Long‐Lived Species , 2013, Conservation biology : the journal of the Society for Conservation Biology.

[23]  Lindsay A. Turnbull,et al.  Identification of 100 fundamental ecological questions , 2013 .

[24]  Boris Schröder,et al.  How to understand species’ niches and range dynamics: a demographic research agenda for biogeography , 2012 .

[25]  Iain Stott,et al.  popdemo: an R package for population demography using projection matrix analysis , 2012 .

[26]  C. Plutzar,et al.  Extinction debt of high-mountain plants under twenty-first-century climate change , 2012 .

[27]  Stuart Townley,et al.  A framework for studying transient dynamics of population projection matrix models. , 2011, Ecology letters.

[28]  R. Ohlemüller,et al.  Rapid Range Shifts of Species Associated with High Levels of Climate Warming , 2011, Science.

[29]  A. Peterson,et al.  The crucial role of the accessible area in ecological niche modeling and species distribution modeling , 2011 .

[30]  D. L. Venable,et al.  The effect of geographic range position on demographic variability in annual plants , 2011 .

[31]  Roberto Salguero-Gómez,et al.  Matrix Dimensions Bias Demographic Inferences: Implications for Comparative Plant Demography , 2010, The American Naturalist.

[32]  William F. Morris,et al.  Demographic compensation and tipping points in climate-induced range shifts , 2010, Nature.

[33]  Elizabeth E Crone,et al.  Causes and consequences of variation in plant population growth rate: a synthesis of matrix population models in a phylogenetic context. , 2010, Ecology letters.

[34]  R. Salguero‐Gómez,et al.  Keeping plant shrinkage in the demographic loop , 2010 .

[35]  R. Holt Bringing the Hutchinsonian niche into the 21st century: Ecological and evolutionary perspectives , 2009, Proceedings of the National Academy of Sciences.

[36]  Jeremy VanDerWal,et al.  Abundance and the Environmental Niche: Environmental Suitability Estimated from Niche Models Predicts the Upper Limit of Local Abundance , 2009, The American Naturalist.

[37]  M. Araújo,et al.  BIOMOD – a platform for ensemble forecasting of species distributions , 2009 .

[38]  K. Barton MuMIn : multi-model inference, R package version 0.12.0 , 2009 .

[39]  H. Pulliam,et al.  Hierarchical analysis of species distributions and abundance across environmental gradients. , 2007, Ecology.

[40]  Brook G. Milligan,et al.  Estimating and Analyzing Demographic Models Using the popbio Package in R , 2007 .

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

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

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

[44]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

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

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

[47]  Paul H. Williams,et al.  Dynamics of extinction and the selection of nature reserves , 2002, Proceedings of the Royal Society of London. Series B: Biological Sciences.

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

[49]  H. Pulliam On the relationship between niche and distribution , 2000 .

[50]  C. Pfister,et al.  Patterns of variance in stage-structured populations: evolutionary predictions and ecological implications. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[51]  J. Silvertown,et al.  Plant Demography and Habitat: A Comparative Approach , 1993 .

[52]  P. Holgate,et al.  Matrix Population Models. , 1990 .

[53]  J. Haldane The relation between density regulation and natural selection , 1956, Proceedings of the Royal Society of London. Series B - Biological Sciences.