Lagged effects of experimental warming and doubled precipitation on annual and seasonal aboveground biomass production in a tallgrass prairie

Sherry, Rebecca A.; Weng, Ensheng; Arnone, John A.; Johnson, Dale W.; Schimel, D.; Verburg, Paul; Wallace, Linda; Luo, Yiqi

doi:10.1111/j.1365-2486.2008.01703.x

Cited by 150 publications

(177 citation statements)

References 66 publications

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“…Whenever possible, direct measurements by biomass harvesting should be preferred over biomass estimation by proxy (Fay et al, 2011;Sherry et al, 2008). 2.…”

Section: Discussionmentioning

confidence: 99%

Estimating aboveground herbaceous plant biomass via proxies: The confounding effects of sampling year and precipitation

Ónodi

Kertész

Kovács-Láng

et al. 2017

Ecological Indicators

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Direct measurements of aboveground plant biomass are often not feasible, thus various biomass proxies are in use. To obtain biomass estimates, these proxies are calibrated against actual biomass, and the resulting proxy-biomass relationship is often used across multiple years and experimental treatments within a study. We investigated how the proxy-biomass relationship varied across years and considered interannual precipitation variability as a contributing factor.We sampled a perennial grassland for ten consecutive years (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) in central Hungary and estimated vegetation cover and Normalized Difference Vegetation Index (NDVI); two frequently used biomass proxies representing two contrasting methods. Aboveground live herbaceous plant biomass was harvested from each plot after sampling, and regression models were used to assess the relationship between biomass proxies and actual aboveground biomass.We found that cover and NDVI were equally effective at estimating biomass. However, the relationship between either biomass proxy and actual biomass varied amongst years, and this was related to the amount of precipitation. In wetter years, proxy-biomass relationships were steeper than in drier years.These results indicate that using the same proxy-biomass relationship across different years or precipitation regimes may not be valid and may introduce systematic error into biomass estimations in long-term studies or precipitation manipulation experiments. Keywords aboveground plant biomass, field spectroscopy, interannual validity, long-term studies, NDVI, precipitation, proxy-biomass relationship, visual cover estimation Nomenclature The Plant List (2010) 4

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“…Whenever possible, direct measurements by biomass harvesting should be preferred over biomass estimation by proxy (Fay et al, 2011;Sherry et al, 2008). 2.…”

Section: Discussionmentioning

confidence: 99%

Estimating aboveground herbaceous plant biomass via proxies: The confounding effects of sampling year and precipitation

Ónodi

Kertész

Kovács-Láng

et al. 2017

Ecological Indicators

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“…Although there are longterm warming experiments (e.g. Saleska et al, 1999;Sherry et al, 2009), we know of no experiments in perennial grasslands that have manipulated growing season rainfall variability and warming for long enough to compare their effects to those of interannual climate variation. Grasslands are important and experimentally tractable systems for examining these issues because they cover approximately 40 % of the land surface, are rich and dynamic in biodiversity, are a globally important agricultural resource, and are at risk from degradation and habitat conversion.…”

Section: Introductionmentioning

confidence: 99%

Relative effects of precipitation variability and warming on tallgrass prairie ecosystem function

et al. 2011

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Abstract. Precipitation and temperature drive many aspects of terrestrial ecosystem function. Climate change scenarios predict increasing precipitation variability and temperature, and long term experiments are required to evaluate the ecosystem consequences of interannual climate variation, increased growing season (intra-annual) rainfall variability, and warming. We present results from an experiment applying increased growing season rainfall variability and year round warming in native tallgrass prairie. During ten years of study, total growing season rainfall varied 2-fold, and we found ∼50-200 % interannual variability in plant growth and aboveground net primary productivity (ANPP), leaf carbon assimilation (A CO 2 ), and soil CO 2 efflux (J CO 2 ) despite only ∼40 % variation in mean volumetric soil water content (0-15 cm, 15 ). Interannual variation in soil moisture was thus amplified in most measures of ecosystem response. Differences between years in 15 explained the greatest portion (14-52 %) of the variation in these processes. Experimentally increased intra-annual season rainfall variability doubled the amplitude of intra-annual soil moisture variation and reduced 15 by 15 %, causing most ecosystem processes to decrease 8-40 % in some or all years with increased rainfall variability compared to ambient rainfall timing, suggesting reduced ecosystem rainfall use efficiency. Warming treatments increased soil temperature at 5 cm depth, particularly during spring, fall, and winter. Warming advanced canopy green up in spring, increased winter J CO 2 , and reduced summer J CO 2 and forb ANPP, suggesting that the effects of warming differed in cooler versus warmer parts of the year. We conclude that (1) major ecosystem processes Correspondence to: P. A. Fay (philip.fay@ars.usda.gov) in this grassland may be substantially altered by predicted changes in interannual climate variability, intra-annual rainfall variability, and temperature, (2) interannual climate variation was a larger source of variation in ecosystem function than intra-annual rainfall variability and warming, and (3) effects of increased growing season rainfall variability and warming were small, but ecologically important. The relative effects of these climate drivers are likely to vary for different ecosystem processes and in wetter or drier ecosystems.

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“…For example, ANPP could exhibit a lagged response to precipitation, with current year production reflecting previous year production in addition to current year precipitation (e.g., Oesterheld et al 2001, Wiegand et al 2004, Arnone et al 2008, Sala et al 2012. Previous authors (Oesterheld et al 2001, Wiegand et al 2004) have demonstrated that lag effects can amplify or dampen precipitation variability, depending on the sequence of precipitation years ( Fig.…”

Section: Introductionmentioning

confidence: 99%

Anticipating changes in variability of grassland production due to increases in interannual precipitation variability

Hsu

Adler

2014

Ecosphere

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Abstract. Expected increases in interannual precipitation variability due to climate change will lead to increases in the variability of primary production, with potentially important consequences for natural resource management. Previous work has suggested that various biotic and abiotic processes might amplify or buffer variation in production in response to variation in precipitation. In particular, production to rain variability ratios (PRVR), the coefficient of variation of production divided by the coefficient of variation of precipitation, indicate that production is often relatively more variable than precipitation. We used 37 long-term data sets from grasslands across the globe to test how future increases in precipitation variability might alter the variability of aboveground net primary production (ANPP). We demonstrate that PRVR is not a useful metric: it is predicted by a site's precipitation-production relationship and a PRVR greater than 1 need not imply that increases in the variability of ANPP will be disproportionately greater than increases in precipitation variability. Instead, it is the form of the precipitation-ANPP relationship that determines how increases in precipitation variability will impact ANPP variability. We fit linear, lag effect, and nonlinear precipitation-ANPP relationships to each data set. At most sites, the precipitation-ANPP relationship is weakly nonlinear, though the lag effect model, incorporating previous year ANPP, performed best at several sites. To test whether the three models project differences in the response of ANPP to future increases in precipitation variability, we directly perturbed the observed precipitation time series and quantified the results of this perturbation on ANPP variability. Under simple linear or lag effect models, relative increases in ANPP variability were always equal to the relative increases in precipitation variability. When we modeled ANPP as a nonlinear, saturating function of precipitation, projected increases in ANPP variability were disproportionately high, with production dropping more in dry years than it increases in wet years. In six cases, increases in ANPP variability were twice as large as increases in precipitation variability. Based on Akaike model weights, a 5% increase in precipitation variability would cause a 6.3% increase in ANPP variability on average.

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Lagged effects of experimental warming and doubled precipitation on annual and seasonal aboveground biomass production in a tallgrass prairie

Cited by 150 publications

References 66 publications

Estimating aboveground herbaceous plant biomass via proxies: The confounding effects of sampling year and precipitation

Estimating aboveground herbaceous plant biomass via proxies: The confounding effects of sampling year and precipitation

Relative effects of precipitation variability and warming on tallgrass prairie ecosystem function

Anticipating changes in variability of grassland production due to increases in interannual precipitation variability

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