Change in Siberian phytomass predicted for global warming.

Tchebakova, N.; Kolchugina, Tatyana P.; Denissenko, Olga V.

doi:10.14214/sf.a9231

Cited by 15 publications

(8 citation statements)

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“…Most research about climate change impacts on plant populations has not taken into account intraspecific genetic variation (Monserud et al ., ; Coops & Waring, ; Nigh et al ., ). Climate change models that predict changes in species productivity or distribution based on climate‐physiological relationships rather than climate‐genetics‐physiological relationships do not consider the intrinsic selection process that influenced population distributions and intraspecific genetic variation.…”

Section: Introductionmentioning

confidence: 99%

Genetic variation in productivity of foundation riparian species at the edge of their distribution: implications for restoration and assisted migration in a warming climate

Grady

Ferrier

Kolb

et al. 2011

Global Change Biology

128

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We examined the hypothesis that genotypic variation among populations of commonly co-occurring phreatophytic trees (Populus fremontii, Salix gooddingii) and the shrub (Salix exigua) regulates aboveground net primary productivity (ANPP) at a hot site at the edge of the species' distribution. We used a provenance trial in which replicated genotypes from populations varying in mean annual temperature were transplanted to a common garden adjacent to the Lower Colorado River in southeastern California. The garden environment represented an extreme maximum temperature for the study species. Four major findings emerged: (1) Genotypic variation in ANPP was significant for all species with broad-sense heritability (H 2 ) across populations of 0.11, 0.13, and 0.10 for P. fremontii, S. gooddingii, and S. exigua, respectively, and within-population H 2 ranging from 0.00 to 0.25, 0.00 to 0.44, and 0.02 to 0.21, respectively.(2) Population ANPP decreased linearly as mean annual maximum temperature (MAMT) transfer distance increased for both P. fremontii (r 2 = 0.64) and S. gooddingii (r 2 = 0.37), whereas it did not change for S. exigua; (3) Populations with similar MAMT to that of the common garden were 1.5 and 1.2 times more productive than populations with 5.0°C MAMT transfer distances for P. fremontii and S. gooddingii, respectively; and (4) Variation in regression slopes among species for the relationship between ANPP and MAMT indicate species-specific responses to temperature. As these plant species characterize a threatened habitat type and support a diverse community that includes endangered species, ecosystem restoration programs should consider using both local genotypes and productive genotypes from warmer environments to maximize productivity of riparian ecosystems in the face of global climate change.

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Section: Introductionmentioning

confidence: 99%

Genetic variation in productivity of foundation riparian species at the edge of their distribution: implications for restoration and assisted migration in a warming climate

Grady

Ferrier

Kolb

et al. 2011

Global Change Biology

128

View full text Add to dashboard Cite

show abstract

“…Several types of models are being used to investigate the environmental controls on vegetation distributions and the potential impacts of climate change, including: several kinds of static, equilibrium models of the climatic controls on vegetation ( Box, 1981; Lenihan & Neilson, 1993; Monserud et al ., 1996 ); simulations of succession and gap‐phase dynamics ( Smith et al ., 1992; Prentice et al 1993; Shao et al 1995 ); and frame‐based simulation models ( Starfield & Chapin, 1996; Chapin & Starfield, 1997). While it is often stated that mechanistic simulation models offer the greatest generality and potential for extrapolation ( Reynolds et al ., 1993 ), they can be very expensive to calibrate and are not practical when much basic data are lacking.…”

Section: Introductionmentioning

confidence: 99%

Using an artificial neural network to characterize the relative suitability of environments for forest types in a complex tropical vegetation mosaic

Hilbert¹,

Muyzenberg²

1999

Diversity and Distributions

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Summary A predictive understanding of the environmental controls on forest distributions is essential for the conservation of biodiversity and management of landscapes in the tropics. This is particularly true now because of potentially rapid climate change. The floristic complexity of tropical forests and the lack or absence of data severely limits the applicability of modelling methods based on the ecology or distribution of individual species. Here we present an artificial neural network (ANN) model using the information available in the humid tropics of North Queensland: a structural classification of forest types, maps of the forest mosaic, and estimates of spatial environmental variables. The ANN model characterizes the relative suitabilities of environments for 15 forest classes defined by their physiognomy and canopy structure. Inputs include seven climate variables, nine soil parent‐material classes, and seven terrain variables. The data used to train the model consisted of a stratified random sample of 75000 points. Output of the model is used to measure the dissimilarity between the environment at each location and the environment that would be most suitable for the forest type that is mapped there. The model is highly successful at distinguishing the relative suitability of environments for the forest classes with 75% of the region's forest mosaic accurately predicted by the model at a one hectare resolution. In contrast, a comparable maximum likelihood classification has an accuracy of only 38%. In the remaining 25% of the region the environments are quite dissimilar to what would be expected for the forest types present there. This is especially the case at boundaries between forest classes and for a transitional forest class. Areas mapped as this disturbed, transitional class are generally classified by the model as having environments suitable to the forest type they are most likely to become. The approach has high potential for the analysis of climate change impacts as well as inferring vegetation patterns in the past and should be applicable wherever vegetation maps and spatial estimates of climate variables are available.

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“…Some reports suggest a general northward shift of forest ecosystems (Emanuel et al, 1985;Thompson et al, 1998). The changes may also result in an invasion of southern species, increasing impacts of pathogens, altered fire regimes and various natural disasters caused by episodic storm events (Shugart et al, 1992, Monserud et al, 1996. The probable destruction of permafrost accompanying climate change (Dyke and Brooks, 2001) and human land use will cause major landscape degradation and loss of biological diversity over large areas (Sala et al, 2000).…”

Section: Climate Changementioning

confidence: 99%

Convention on Biological Diversity

2013

Choice Reviews Online

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Change in Siberian phytomass predicted for global warming.

Cited by 15 publications

References 0 publications

Genetic variation in productivity of foundation riparian species at the edge of their distribution: implications for restoration and assisted migration in a warming climate

Genetic variation in productivity of foundation riparian species at the edge of their distribution: implications for restoration and assisted migration in a warming climate

Using an artificial neural network to characterize the relative suitability of environments for forest types in a complex tropical vegetation mosaic

Convention on Biological Diversity

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