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1.
Oecologia ; 187(3): 839-849, 2018 07.
Artigo em Inglês | MEDLINE | ID: mdl-29767812

RESUMO

The use of species distribution as a climate proxy for ecological forecasting is thought to be acceptable for invasive species. Kudzu (Pueraria montana var. lobata) is an important invasive whose northern distribution appears to be limited by winter survival; however, kudzu's cold tolerance thresholds are uncertain. Here, we used biogeographic evidence to hypothesize that exposure to - 20 °C is lethal for kudzu and thus determines its northern distribution limit. We evaluated this hypothesis using survival tests and electrolyte leakage to determine relative conductivity, a measure of cell damage, on 14 populations from eastern North America. Relative conductivity above 36% was lethal. Temperatures causing this damage averaged - 19.6 °C for northern and - 14.4 °C for southern populations, indicating kudzu acclimates to winter cold. To assess this, we measured relative conductivity of above- and belowground stems, and roots collected throughout the winter at a kudzu population in southern Ontario, Canada. Consistent with acclimation, the cold tolerance threshold of aboveground stems at the coldest time of year was - 26 °C, while stems insulated from cold extremes survived to - 17 °C-colder than the survival limits indicated by kudzu's biogeographic distribution. While these results do not rule out alternative cold limitations, they indicate kudzu can survive winters north of its current distribution. For kudzu, biogeography is not a proxy for climatic tolerance and continued northward migration is possible. Efforts to limit its spread are therefore prudent. These results demonstrate that physiological constraints inform predictions of climate-related changes in species distribution and should be considered where possible.


Assuntos
Pueraria , Aclimatação , Temperatura Baixa , Ontário , Estações do Ano
2.
PLoS One ; 4(4): e5102, 2009.
Artigo em Inglês | MEDLINE | ID: mdl-19352494

RESUMO

Climate change is expected to alter the geographic distribution of wildfire, a complex abiotic process that responds to a variety of spatial and environmental gradients. How future climate change may alter global wildfire activity, however, is still largely unknown. As a first step to quantifying potential change in global wildfire, we present a multivariate quantification of environmental drivers for the observed, current distribution of vegetation fires using statistical models of the relationship between fire activity and resources to burn, climate conditions, human influence, and lightning flash rates at a coarse spatiotemporal resolution (100 km, over one decade). We then demonstrate how these statistical models can be used to project future changes in global fire patterns, highlighting regional hotspots of change in fire probabilities under future climate conditions as simulated by a global climate model. Based on current conditions, our results illustrate how the availability of resources to burn and climate conditions conducive to combustion jointly determine why some parts of the world are fire-prone and others are fire-free. In contrast to any expectation that global warming should necessarily result in more fire, we find that regional increases in fire probabilities may be counter-balanced by decreases at other locations, due to the interplay of temperature and precipitation variables. Despite this net balance, our models predict substantial invasion and retreat of fire across large portions of the globe. These changes could have important effects on terrestrial ecosystems since alteration in fire activity may occur quite rapidly, generating ever more complex environmental challenges for species dispersing and adjusting to new climate conditions. Our findings highlight the potential for widespread impacts of climate change on wildfire, suggesting severely altered fire regimes and the need for more explicit inclusion of fire in research on global vegetation-climate change dynamics and conservation planning.


Assuntos
Incêndios , Geografia , Clima , Modelos Estatísticos , Análise de Regressão
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