Reconciling inconsistencies in precipitation–productivity relationships implications for climate change

Precipitation (PPT) is a primary climatic determinant of plant growth and aboveground net primary production (ANPP) over much of the globe. Thus, PPT–ANPP relationships are important both ecologically and to land–atmosphere models that couple terrestrial vegetation to the global carbon cycle. Empiri...

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Veröffentlicht in:	The New phytologist Jg. 214; H. 1; S. 41 - 47
Hauptverfasser:	Knapp, Alan K., Ciais, Philippe, Smith, Melinda D.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England New Phytologist Trust 01.04.2017 Wiley Subscription Services, Inc Wiley
Schlagworte:	Arid zones Atmosphere Biogeochemistry Carbon cycle Climate Change climate extremes Continental interfaces, environment Data collection Drought Ecological function Ecosystem structure ecosystems Environmental impact Extreme drought Grasslands Hydrologic cycle Models, Theoretical Moisture content Nonlinear Dynamics nonlinear models Ocean, Atmosphere Plant biomass Plant Development Plant growth precipitation Primary production primary productivity productivity Rain Sciences of the Universe Semiarid lands Soil water Statistical models Tansley insight Terrestrial ecosystems Terrestrial environments Time Factors Variability vegetation Vegetation patterns Water use precipitation productivity drought carbon cycle variability climate change climate extremes
ISSN:	0028-646X, 1469-8137
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Abstract	Precipitation (PPT) is a primary climatic determinant of plant growth and aboveground net primary production (ANPP) over much of the globe. Thus, PPT–ANPP relationships are important both ecologically and to land–atmosphere models that couple terrestrial vegetation to the global carbon cycle. Empirical PPT–ANPP relationships derived from long-term site-based data are almost always portrayed as linear, but recent evidence has accumulated that is inconsistent with an underlying linear relationship. We review, and then reconcile, these inconsistencies with a nonlinear model that incorporates observed asymmetries in PPT–ANPP relationships. Although data are currently lacking for parameterization, this new model highlights research needs that, when met, will improve our understanding of carbon cycle dynamics, as well as forecasts of ecosystem responses to climate change.
AbstractList	Contents 41 I. 41 II. 42 III. 43 IV. 44 V. 45 Acknowledgements 46 References 46 SUMMARY: Precipitation (PPT) is a primary climatic determinant of plant growth and aboveground net primary production (ANPP) over much of the globe. Thus, PPT-ANPP relationships are important both ecologically and to land-atmosphere models that couple terrestrial vegetation to the global carbon cycle. Empirical PPT-ANPP relationships derived from long-term site-based data are almost always portrayed as linear, but recent evidence has accumulated that is inconsistent with an underlying linear relationship. We review, and then reconcile, these inconsistencies with a nonlinear model that incorporates observed asymmetries in PPT-ANPP relationships. Although data are currently lacking for parameterization, this new model highlights research needs that, when met, will improve our understanding of carbon cycle dynamics, as well as forecasts of ecosystem responses to climate change. Precipitation (PPT) is a primary climatic determinant of plant growth and aboveground net primary production (ANPP) over much of the globe. Thus, PPT–ANPP relationships are important both ecologically and to land–atmosphere models that couple terrestrial vegetation to the global carbon cycle. Empirical PPT–ANPP relationships derived from long-term site-based data are almost always portrayed as linear, but recent evidence has accumulated that is inconsistent with an underlying linear relationship. We review, and then reconcile, these inconsistencies with a nonlinear model that incorporates observed asymmetries in PPT–ANPP relationships. Although data are currently lacking for parameterization, this new model highlights research needs that, when met, will improve our understanding of carbon cycle dynamics, as well as forecasts of ecosystem responses to climate change. Precipitation ( PPT ) is a primary climatic determinant of plant growth and aboveground net primary production ( ANPP ) over much of the globe. Thus, PPT – ANPP relationships are important both ecologically and to land–atmosphere models that couple terrestrial vegetation to the global carbon cycle. Empirical PPT – ANPP relationships derived from long‐term site‐based data are almost always portrayed as linear, but recent evidence has accumulated that is inconsistent with an underlying linear relationship. We review, and then reconcile, these inconsistencies with a nonlinear model that incorporates observed asymmetries in PPT – ANPP relationships. Although data are currently lacking for parameterization, this new model highlights research needs that, when met, will improve our understanding of carbon cycle dynamics, as well as forecasts of ecosystem responses to climate change. Contents Summary 41 I. Introduction 41 II. The PPT–ANPP relationship: spatial vs temporal models 42 III. Inconsistencies with a linear temporal model 43 IV. Revision of the temporal PPT–ANPP model to better forecast climate change impacts 44 V. Conclusions 45 Acknowledgements 46 References 46 See also the Commentary on this article by Luo et al. , 214 : 5–7 . Precipitation (PPT) is a primary climatic determinant of plant growth and aboveground net primary production (ANPP) over much of the globe. Thus, PPT-ANPP relationships are important both ecologically and to land-atmosphere models that couple terrestrial vegetation to the global carbon cycle. Empirical PPT-ANPP relationships derived from long-term site-based data are almost always portrayed as linear, but recent evidence has accumulated that is inconsistent with an underlying linear relationship. We review, and then reconcile, these inconsistencies with a nonlinear model that incorporates observed asymmetries in PPT-ANPP relationships. Although data are currently lacking for parameterization, this new model highlights research needs that, when met, will improve our understanding of carbon cycle dynamics, as well as forecasts of ecosystem responses to climate change. See also the Commentary on this article by Luo et al., 214: 5-7 . Contents 41 I. 41 II. 42 III. 43 IV. 44 V. 45 Acknowledgements 46 References 46 Summary Precipitation (PPT) is a primary climatic determinant of plant growth and aboveground net primary production (ANPP) over much of the globe. Thus, PPT–ANPP relationships are important both ecologically and to land–atmosphere models that couple terrestrial vegetation to the global carbon cycle. Empirical PPT–ANPP relationships derived from long‐term site‐based data are almost always portrayed as linear, but recent evidence has accumulated that is inconsistent with an underlying linear relationship. We review, and then reconcile, these inconsistencies with a nonlinear model that incorporates observed asymmetries in PPT–ANPP relationships. Although data are currently lacking for parameterization, this new model highlights research needs that, when met, will improve our understanding of carbon cycle dynamics, as well as forecasts of ecosystem responses to climate change. See also the Commentary on this article by Luo et al., 214: 5–7. 41 I. I. Introduction Terrestrial ecosystems cover less than one-third of the Earth's surface, yet account for approximately two-thirds of global net primary production (NPP), with the carbon (C) resident in terrestrial plant biomass equivalent to c. 70% of that in the atmosphere (Houghton, ). The aboveground fraction of NPP (ANPP) is particularly important both as an integrator of terrestrial ecosystem function (Fahey & Knapp, ), and because humans depend on ANPP for food, fiber and fuel (Haberl et al., ). Thus, in this era of unprecedented climate change, an understanding of the primary controls of ANPP is important from climatological, ecological and socioeconomic perspectives. There is a rich history of foundational studies documenting water availability as a key determinant of spatial and temporal patterns of ANPP (e.g. Rosenzweig, ; Webb et al., ), and more recent analyses have confirmed that precipitation (PPT) inputs, or alternative measures of water availability, limit or co-limit ANPP, as well as gross primary production (GPP), over much of the globe (Fig. ; Nemani et al., ; Garbulsky et al., ; Ahlström et al., ; Seddon et al., ). Of course, some biomes are more strongly limited by water than others (Fig. ) but, even in cold or humid regions, in which temperature or other factors are expected to more strongly limit ANPP, ecosystems are surprisingly sensitive to variations in water availability, directly or indirectly (Schuur, ; Vicente-Serrano et al., ; Winkler et al., ). Thus, forecast changes in PPT are predicted to have significant impacts on ANPP from local scales that extend to the global C cycle (Reichstein et al., ). While a warming atmosphere is the most certain climatic change occurring, the global hydrological cycle has been forecast to intensify as well (IPCC, ). This intensification may be manifest in many ways, including increased interannual PPT variability, more frequent extreme PPT years (wet and dry) and alterations in annual PPT amount, with some regions expected to become wetter and others drier (Lau et al., ; Polade et al., ). Recent climatological trends have supported these predictions (Huntington, ; Fischer & Knutti, ; Hubbart et al., ). Thus, forecasting how future ecosystem structure and function will respond to changing PPT regimes requires a robust understanding of the relationship between PPT and ANPP. Key to such forecasts is the presumption that the contemporary form of the PPT-ANPP relationship is appropriate for the prediction of responses to climate change. In the last two decades, ecologists have conducted scores of analyses of long-term ANPP datasets, and modeling and experimental studies, to provide insights into how ANPP (and other C cycle components) will respond to future PPT regimes. Although much has been learned, this body of research includes a number of results that are inconsistent with one another and our current understanding of the PPT-ANPP relationship. This lack of clarity with regard to how ANPP responds to a primary climatic control suggests that the PPT-ANPP relationship needs to be revisited. Below, we summarize recent research results with an emphasis on discrepancies between what has been observed vs expected based on our contemporary understanding of the PPT-ANPP relationship. We then reconcile these inconsistencies with a new conceptual model for the PPT-ANPP relationship, one which highlights research needs that, when addressed, will improve forecasts of C cycle responses to future changes in PPT. 41 II. II. The PPT-ANPP relationship: spatial vs temporal models The PPT-ANPP relationship is typically derived from multi-year measurements of PPT and ANPP, and is viewed through either a spatial or temporal lens. This has led to two distinct models: spatial models based on ANPP data combined from many sites arrayed along PPT gradients, and temporal models derived from individual sites in which PPT and ANPP have varied over time (Fig. ). These two models are often related because spatial models are usually based on mean values from site-based temporal models (Huxman et al., ). Statistical relationships for spatial models are usually nonlinear (concave down or saturating, Fig. ) when they span large gradients in PPT, although these can be linear when models are restricted to a single biome (Fig. , e.g. grasslands - Sala et al., ). Temporal PPT-ANPP relationships from long-term site-level data are almost always portrayed as linear regardless of the ecosystem type (Fig. ). Although more complex nonlinear statistical models have been fitted to some PPT-ANPP relationships, in most cases, linear and nonlinear models explain equal amounts of variation, nonlinearities tend to be weak, and support for nonlinear over linear models is minimal (Hsu et al., ; Hsu & Adler, ). Spatial and temporal models often share the same data, but the slopes of spatial relationships are usually much steeper than those of temporal models (Fig. ). Thus, temporal models predict that ANPP will be less sensitive to future changes in PPT than do spatial models (Estiarte et al., ). Several mechanisms have been posited to explain why spatial models predict greater sensitivity of ANPP to PPT. The most likely is that spatial models include both vegetation and PPT change along gradients of PPT, whereas vegetation does not change appreciably over time in temporal models. This places a 'vegetation constraint' (Lauenroth & Sala, ) on ANPP responses to PPT at the site level. For example, plants in arid ecosystems tend to be smaller, with inherently slower absolute growth rates and reduced plant and meristem densities relative to those in more mesic ecosystems (Knapp & Smith, ; Huxman et al., ; La Pierre et al., ). As a result, ANPP responses to wet years in arid ecosystems are constrained by these plant community characteristics. Indeed, Gaitan et al. estimated that two-thirds of the increase in ANPP along regional PPT gradients in Patagonia could be attributed to changes in plant communities and not to direct responses to increased PPT. In addition, Sala et al. argued that 'legacy effects' of previous year's PPT on current year's ANPP are widespread. In this case, previous wet or dry years can dampen ANPP responses in subsequent years and reduce the slope of site-based PPT-ANPP relationships. Despite differences in sensitivity to PPT, both models predict that the sensitivity of ANPP to PPT decreases from dry to wet ecosystems, as a result of increasing biogeochemical limitations of ANPP as ecosystems get wetter (Huxman et al., ). Although climate change is expected to affect plant community and biogeochemical constraints on ANPP, both of which are implicitly incorporated into empirically derived spatial models, there is little evidence that spatial models are superior to temporal models for the prediction of ANPP responses to future changes in PPT (Estiarte et al., ; Wilcox et al., ). For example, when predictions from temporal vs spatial models were compared with results from multiyear PPT manipulation experiments, temporal models performed consistently better (Estiarte et al., ). This is probably because substantial changes in plant communities (turnover of dominant life-forms) and corresponding alterations in soil biogeochemistry only occur over very long time scales (decades to centuries; Smith et al., ; Wilcox et al., ). However, even over long time scales, the novelty of future climates and interactions with other global change drivers are expected to lead to communities that do not match current climate-vegetation patterns (Zarnetske et al., ). Thus, at least for near to mid-term (decade to century) forecasts of climate change effects on ANPP, temporal models are preferred over spatial models (Estiarte et al., ). These temporal models are the focus of the remainder of this review. 42 III. III. Inconsistencies with a linear temporal model Evidence for positive asymmetry Despite the near-universal use of linear models to describe the temporal relationship between PPT and ANPP (Fig. ), results from a number of recent studies are inconsistent with an underlying linear relationship (Fig. ). For example, when long-term PPT-ANPP relationships from multiple biomes were assessed, maximum ANPP values in response to high PPT years deviated more from the long-term mean than did minimum ANPP values in low PPT years (Knapp & Smith, ). This positive asymmetry in maximum vs minimum ANPP responses to PPT could not be explained by corresponding asymmetry in PPT, and suggests that ANPP in these ecosystems responded more to wet than dry years. Subsequent analyses of long-term data from > 100 additional sites revealed similar patterns of asymmetry worldwide (Fig. ). Experimental manipulations of PPT offer further support for positive asymmetry in ANPP responses to PPT. Wu et al. synthesized results from 28 experiments and reported that ANPP was much more sensitive to increased than decreased PPT (Fig. ). Unger & Jongen reported similar patterns from an even larger number of experiments. Further, they noted that positive asymmetry in ANPP responses to PPT was particularly pronounced in arid and semi-arid regions, as did Knapp & Smith . Ahlström et al. reported positive asymmetries in GPP in semi-arid ecosystems as well. Taken together, these observational and experimental analyses are inconsistent with an underlying linear model describing the PPT-ANPP relationship. Mechanisms that may lead to positive asymmetric responses in ANPP to PPT are varied. These include maintenance of ANPP during dry years as a result of the carry-over of soil water from previous years (Sala et al., ), as well as plants increasing water use efficiency during drought years (Gutschick & BassiriRad, ; Huxman et al., ). In wet years, other resources (e.g. soil nutrients) may be increased in concert with PPT, leading to higher than expected ANPP (Seastedt & Knapp, ). In addition, wet years are characterized by more numerous large P
Author	Alan K. Knapp Melinda D. Smith Philippe Ciais
Author_xml	– sequence: 1 givenname: Alan K. surname: Knapp fullname: Knapp, Alan K. email: aknapp@colostate.edu organization: Colorado State University – sequence: 2 givenname: Philippe surname: Ciais fullname: Ciais, Philippe organization: CEA CNRS UVSQ – sequence: 3 givenname: Melinda D. surname: Smith fullname: Smith, Melinda D. organization: Colorado State University
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Snippet	Precipitation (PPT) is a primary climatic determinant of plant growth and aboveground net primary production (ANPP) over much of the globe. Thus, PPT–ANPP... Contents 41 I. 41 II. 42 III. 43 IV. 44 V. 45 Acknowledgements 46 References 46 Summary Precipitation (PPT) is a primary climatic determinant of plant growth... Precipitation ( PPT ) is a primary climatic determinant of plant growth and aboveground net primary production ( ANPP ) over much of the globe. Thus, PPT –... Contents 41 I. 41 II. 42 III. 43 IV. 44 V. 45 Acknowledgements 46 References 46 SUMMARY: Precipitation (PPT) is a primary climatic determinant of plant growth... 41 I. I. Introduction Terrestrial ecosystems cover less than one-third of the Earth's surface, yet account for approximately two-thirds of global net primary... Precipitation (PPT) is a primary climatic determinant of plant growth and aboveground net primary production (ANPP) over much of the globe. Thus, PPT-ANPP...
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SubjectTerms	Arid zones Atmosphere Biogeochemistry Carbon cycle Climate Change climate extremes Continental interfaces, environment Data collection Drought Ecological function Ecosystem structure ecosystems Environmental impact Extreme drought Grasslands Hydrologic cycle Models, Theoretical Moisture content Nonlinear Dynamics nonlinear models Ocean, Atmosphere Plant biomass Plant Development Plant growth precipitation Primary production primary productivity productivity Rain Sciences of the Universe Semiarid lands Soil water Statistical models Tansley insight Terrestrial ecosystems Terrestrial environments Time Factors Variability vegetation Vegetation patterns Water use
Subtitle	implications for climate change
Title	Reconciling inconsistencies in precipitation–productivity relationships
URI	https://www.jstor.org/stable/90001550 https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fnph.14381 https://www.ncbi.nlm.nih.gov/pubmed/28001290 https://www.proquest.com/docview/1884084605 https://www.proquest.com/docview/1851304785 https://www.proquest.com/docview/1877818701 https://www.proquest.com/docview/2000494805 https://hal.science/hal-02904608
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