Predicting the likely response of data‐poor ecosystems to climate change using space‐for‐time substitution across domains

Predicting ecological response to climate change is often limited by a lack of relevant local data from which directly applicable mechanistic models can be developed. This limits predictions to qualitative assessments or simplistic rules of thumb in data-poor regions, making management of the relevant systems difficult. We demonstrate a method for developing quantitative predictions of ecological response in data-poor ecosystems based on a space-for-time substitution, using distant, well-studied systems across an inherent climatic gradient to predict ecological response. Changes in biophysical data across the spatial gradient are used to generate quantitative hypotheses of temporal ecological responses that are then tested in a target region. Transferability of predictions among distant locations, the novel outcome of this method, is demonstrated via simple quantitative relationships that identify direct and indirect impacts of climate change on physical, chemical and ecological variables using commonly available data sources. Based on a limited subset of data, these relationships were demonstrably plausible in similar yet distant (>2000 km) ecosystems. Quantitative forecasts of ecological change based on climate-ecosystem relationships from distant regions provides a basis for research planning and informed management decisions, especially in the many ecosystems for which there are few data. This application of gradient studies across domains - to investigate ecological response to climate change - allows for the quantification of effects on potentially numerous, interacting and complex ecosystem components and how they may vary, especially over long time periods (e.g. decades). These quantitative and integrated long-term predictions will be of significant value to natural resource practitioners attempting to manage data-poor ecosystems to prevent or limit the loss of ecological value. The method is likely to be applicable to many ecosystem types, providing a robust scientific basis for estimating likely impacts of future climate change in ecosystems where no such method currently exists.

[1]  S. Brewer,et al.  Backcasting the decline of a vulnerable Great Plains reproductive ecotype: identifying threats and conservation priorities , 2014, Global change biology.

[2]  J. Bruno,et al.  The Impact of Climate Change on the World’s Marine Ecosystems , 2010, Science.

[3]  Richard C. Thompson,et al.  Complex interactions in a rapidly changing world: responses of rocky shore communities to recent climate change , 2008 .

[4]  S. Williams,et al.  Current Analogues of Future Climate Indicate the Likely Response of a Sensitive Montane Tropical Avifauna to a Warming World , 2013, PloS one.

[5]  J. Kerr,et al.  Historically calibrated predictions of butterfly species' range shift using global change as a pseudo-experiment. , 2009, Ecology.

[6]  L. Rustad.  The response of terrestrial ecosystems to global climate change: towards an integrated approach. , 2008, The Science of the total environment.

[7]  Stephen T. Jackson,et al.  MODERN ANALOGS IN QUATERNARY PALEOECOLOGY: Here Today, Gone Yesterday, Gone Tomorrow? , 2004 .

[8]  P. Durack,et al.  Estimating the impacts of climate change on Victoria’srunoff using a hydrological sensitivity model , 2005 .

[9]  Predicting Novel Riparian Ecosystems in a Changing Climate , 2013, Ecosystems.

[10]  Tadashi Fukami,et al.  Long-term ecological dynamics: reciprocal insights from natural and anthropogenic gradients , 2005, Proceedings of the Royal Society B: Biological Sciences.

[11]  T. Wernberg,et al.  A decade of climate change experiments on marine organisms: procedures, patterns and problems , 2012 .

[12]  Steward T. A. Pickett,et al.  Space-for-Time Substitution as an Alternative to Long-Term Studies , 1989 .

[13]  P. Hesp,et al.  Estuaries to salt lakes: Holocene transformation of the estuarine ecosystems of south-western Australia , 1998 .

[14]  Dolors Vaqué,et al.  Warming effects on marine microbial food web processes: how far can we go when it comes to predictions? , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[15]  I. Côté,et al.  Quantifying the evidence for ecological synergies. , 2008, Ecology letters.

[16]  B. Cade,et al.  Assessing conservation relevance of organism–environment relations using predicted changes in response variables , 2010 .

[17]  H. Akaike.  Maximum likelihood identification of Gaussian autoregressive moving average models , 1973 .

[18]  L. Mermet,et al.  The rise of research on futures in ecology: rebalancing scenarios and predictions. , 2009, Ecology letters.

[19]  P. Wookey.  Experimental approaches to predicting the future of tundra plant communities , 2008 .

[20]  Richard C. Thompson,et al.  Consequences of climate-driven biodiversity changes for ecosystem functioning of North European rocky shores , 2009 .

[21]  Richard M Cowling,et al.  Conservation planning in a changing world. , 2007, Trends in ecology & evolution.

[22]  A. Newton,et al.  Toward integrated analysis of human impacts on forest biodiversity: lessons from Latin America. , 2009 .

[23]  James S. Clark,et al.  Coordinated approaches to quantify long‐term ecosystem dynamics in response to global change , 2011 .

[24]  N. LeRoy Poff,et al.  The ecological limits of hydrologic alteration (ELOHA): a new framework for developing regional environmental flow standards , 2007 .

[25]  V. Vandvik,et al.  Climate change affects the outcome of competitive interactions—an application of principal response curves , 2004, Oecologia.

[26]  Xiao-Li Meng,et al.  POSTERIOR PREDICTIVE ASSESSMENT OF MODEL FITNESS VIA REALIZED DISCREPANCIES , 1996 .

[27]  R. Naiman,et al.  The challenge of providing environmental flow rules to sustain river ecosystems. , 2006, Ecological applications : a publication of the Ecological Society of America.

[28]  Fiona J. Valesini,et al.  Potential effects of climate change on Australian estuaries and fish utilising estuaries: a review , 2011 .

[29]  E. Johnson,et al.  Testing the assumptions of chronosequences in succession. , 2008, Ecology letters.

[30]  V. J. Wearmouth,et al.  Body size‐dependent responses of a marine fish assemblage to climate change and fishing over a century‐long scale , 2010 .

[31]  S. Hawkins,et al.  MODELING THE RESPONSE OF POPULATIONS OF COMPETING SPECIES TO CLIMATE CHANGE. , 2008, Ecology.

[32]  G. Kendrick,et al.  Decreasing resilience of kelp beds along a latitudinal temperature gradient: potential implications for a warmer future. , 2010, Ecology letters.

[33]  T. D. Harrison,et al.  Estuarine Typology and the Structuring of Fish Communities in South Africa , 2006, Environmental Biology of Fishes.

[34]  Giuseppe Del Re,et al.  Models and analogies in science , 2013 .

[35]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[36]  J. Kerr,et al.  The Macroecological Contribution to Global Change Solutions , 2007, Science.

[37]  J. Vaze,et al.  Estimation of Climate Change Impact on Mean Annual Runoff across Continental Australia Using Budyko and Fu Equations and Hydrological Models , 2012 .

[38]  William J. Sutherland,et al.  Predicting the ecological consequences of environmental change: a review of the methods* , 2006 .

[39]  P. C. Reid,et al.  Impacts of climate change on European marine ecosystems: Observations, expectations and indicators , 2011 .

[40]  T. Hothorn,et al.  Multivariate Normal and t Distributions , 2016 .

[41]  C. Harley,et al.  On the prediction of extreme ecological events , 2009 .

[42]  C. Parmesan.  Ecological and Evolutionary Responses to Recent Climate Change , 2006 .