Editing IPCC:AR6/WGII/Chapter-2 (section)

==== 2.4.3.1 Detection and Attribution for Observed Biome Shifts ====

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Attribution for biome (major vegetation form of an ecosystem) shifts is complex because of their extensive, sometimes continental, spatial scale ( [[#Whittaker--1975|Whittaker, 1975]] ; [[#Olson--2001|Olson et al., 2001]] ; [[#Woodward--2004|Woodward et al., 2004]] ). Therefore, non-climatic factors strongly influence biome spatial distributions ( [[#Ellis--2008|Ellis and Ramankutty, 2008]] ).

The most robust attribution studies use data from many species, individual locations with minimal confounding factors, particularly observed recent LULCC, and scale up by analysing multiple locations across a large zone between biomes, providing multiple lines of evidence ( [[#Hegerl--2010|Hegerl et al., 2010]] ; [[#Parmesan--2013|Parmesan et al., 2013]] ). Multivariate statistical analyses aid attribution studies by allowing the assessment of relative weights among multiple factors, including variables related to climate change ( [[#Gonzalez--2012|Gonzalez et al., 2012]] ). However, drivers often have strong, significant interactions with one another, complicating quantitative assessment of the strength of individual drivers ( [[#Parmesan--2013|Parmesan et al., 2013]] ). In these cases, manipulative experiments are critical in assessing attribution to the drivers of climate change.

Certain biomes exhibit a relatively stronger relationship to climate; for example, Arctic tundra generally has a distinct ecotone with boreal conifer forest ( [[#Whittaker--1975|Whittaker, 1975]] ). In these areas, attribution of biome shifts to climate change are relatively straightforward, if human LULCC is minimal. However, other biomes, such as many grassland systems, are not in equilibrium with climate ( [[#Bond--2005|Bond et al., 2005]] ). In these systems, their evolutionary history ( [[#Keeley--2011|Keeley et al., 2011]] ; [[#Strömberg--2011|Strömberg, 2011]] ; [[#Charles-Dominique--2016|Charles-Dominique et al., 2016]] ), distribution, structure and function have been shaped by climate and natural disturbances, such as fire and herbivory ( [[#Staver--2011|Staver et al., 2011]] ; [[#Lehmann--2014|Lehmann et al., 2014]] ; [[#Pausas--2015|Pausas, 2015]] ; [[#Bakker--2016|Bakker et al., 2016]] ; [[#Malhi--2016|Malhi et al., 2016]] ). Disturbance variability is an inherent characteristic of grassland systems, and suitable ‘control’ conditions are seldom available in nature. Furthermore, due to the integral role of disturbance, these biomes have been widely affected by long-term and widespread shifts in grazing regimes, large-scale losses of mega-herbivores and fire suppression policies ( [[#Archibald--2013|Archibald et al., 2013]] ; [[#Malhi--2016|Malhi et al., 2016]] ; [[#Hempson--2017|Hempson et al., 2017]] ). It is necessary to conduct climate change attribution on a case-by-case basis for grasslands; such assessments are complex as direct climate change impacts from either inherent variation within disturbance regimes or directional changes in background disturbances are difficult to separate (detailed in Sections 2.4.3.2.1; 2.4.3.2.2; 2.4.3.5). Confidence in assessments is increased when the observed trends are supported by a mechanistic understanding of responses identified by physiological studies, manipulative field experiments, greenhouse studies and lab experiments (Table SM2.1).

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