Incident radiation and the allocation of nitrogen within Arctic plant canopies: implications for predicting gross primary productivity

Abstract

Arctic vegetation is characterised by high spatial variability in plant functional type (PFT) 3 composition and gross primary productivity (P). Despite this variability, the two main drivers of 4 P in sub-Arctic tundra are leaf area index (L T ) and total foliar nitrogen (N T ). L T and N T have been 5shown to be tightly coupled across PFTs in sub-Arctic tundra vegetation, which simplifies up-6 scaling by allowing quantification of the main drivers of P from remotely sensed L T . Our 7 objective was to test the L T -N T relationship across multiple Arctic latitudes and to assess L T as a 8 predictor of gross primary productivity (P) for the pan-Arctic. Including PFT specific parameters 9 in models of L T -N T coupling provided only incremental improvements in model fit, but 10 significant improvements were gained from including site-specific parameters. The degree of 11 curvature in the L T -N T relationship, controlled by a fitted canopy nitrogen extinction co-efficient, 12 was negatively related to average levels of diffuse radiation at a site. This is consistent with 13 theoretical predictions of more uniform vertical canopy N distributions under diffuse light 14 conditions. Higher latitude sites had higher average leaf N content by mass (N M ), and we show 15 for the first time that L T -N T coupling is achieved across latitudes via canopy scale trade-offs 16 between N M and leaf mass per unit leaf area (L M ). Site-specific parameters provided small but 17 significant improvements in models of P based on L T and moss cover. Our results suggest that 18 differences in L T -N T coupling between sites could be used to improve pan-Arctic models of P 19 and we provide unique evidence that prevailing radiation conditions can significantly affect N 20 allocation over regional scales.

Pan-Arctic modelling of net ecosystem exchange of CO 2

“…These were (i) the research plots of the US Arctic LTER project [17], (ii) nearby Imnavait Creek [18], and (iii) the 2007 Anaktuvuk River (AR) Wildfire about 40 km north of Toolik Lake [19 -21]. -Abisko, Sweden, including three subsites within 15 km of the Abisko Scientific Research Station [13,14]. These were 'Paddus', 'Stepps' and 'Latnjajaure'.…”

“…These were 'Paddus', 'Stepps' and 'Latnjajaure'. -Longyearbyen, Svalbard, including ecosystem types within the Adventalen valley [14]. -Zackenberg, Greenland, including ecosystem types within 2 km of the Zackenberg Ecological Research Station [14].…”

“…-Zackenberg, Greenland, including ecosystem types within 2 km of the Zackenberg Ecological Research Station [14]. -Barrow, Alaska, including a ecosystem types within the Barrow Environmental Observatory [14].…”

“…[13]; (3) Williams et al [8]; (4) Street et al [14] and (5) In addition to PAR and air temperature measurements that accompanied each measurement of NEE, for each light response curve, we also made four to six measurements of soil temperature at 5 cm depth using a digital soil thermometer. Additional measurements included estimates of plant cover by species and measurements of soil moisture using a Hydrosense soil moisture probe (Campbell Scientific, Logan, UT, USA); for many plots, we also measured soil temperature at the surface and at 10 cm depth and depth of soil thaw.…”

“…For example, a higher Q 10 might be expected in colder environments of the high Arctic [27], although Q 10 values this high have never been reported for Arctic ecosystems. Changes in leaf N and leaf area-leaf N relationships might be partly responsible for the higher P maxL in the high Arctic [14,28]. However, one key difference between the low and high Arctic datasets is that the high Arctic data include a much narrower range of observed values of NEE, LAI, PAR and air temperature on which the regression is based.…”