WilliamsKharoubaVelozEtAl2013

Référence

Williams, J.W., Kharouba, H.M., Veloz, S., Vellend, M., Mclachlan, J., Liu, Z., Otto-Bliesner, B. and He, F. (2013) The ice age ecologist: Testing methods for reserve prioritization during the last global warming. Global Ecology and Biogeography, 22(3):289-301. (Scopus )

Résumé

Aim We play the role of an ice age ecologist (IAE) charged with conserving biodiversity during the climate changes accompanying the last deglaciation. We develop reserve-selection strategies for the IAE and check them against rankings based on modern data. Location Northern and eastern North America. Methods Three reserve-selection strategies are developed. (1) Abiotic: the IAE uses no information about species-climate relationships, instead maximizing the climatic and geographic dispersion of reserves. (2) Species distribution models (SDMs): the IAE uses boosted-regression trees calibrated against pollen data and CCSM3 palaeoclimatic simulations from 21 to 15 ka bp to predict modern taxon distributions, then uses these as input to the Zonation reserve-ranking program. (3) Rank-and-regress: regression models are used to identify climatic predictors of zonation rankings. All strategies are assessed against a Zonation ranking based on modern pollen distributions. Analysis units are ecoregions and grid cells. Results The abiotic strategy has a negative or no correlation between predicted and actual rankings. The SDM-based strategy fares better, with a significantly positive area-corrected correlation (r= 0.474, P < 0.001) between predicted and actual rankings. Predictive ability drops when grid cells are the analysis unit (r= 0.217, P = 0.058). Predictive ability for the rank-and-regress strategy is similar to the SDM results. Main conclusions For the IAE, SDMs improve the predictive ability of reserve-selection strategies. However, predictive ability is limited overall, probably due to shifted realized niches during past no-analogue climates, new species interactions as species responded individually to climate change, and other environmental changes not included in the model. Twenty-first-century conservation planning also faces these challenges, and is further complicated by other anthropogenic impacts. The IAE's limited success does not preclude the use of climate scenarios and niche-based SDMs when developing adaptation strategies, but suggests that such tools offer at best only a rough guide to identifying possible areas of future conservation value. © 2012 Blackwell Publishing Ltd.

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@ARTICLE { WilliamsKharoubaVelozEtAl2013,
    AUTHOR = { Williams, J.W. and Kharouba, H.M. and Veloz, S. and Vellend, M. and Mclachlan, J. and Liu, Z. and Otto-Bliesner, B. and He, F. },
    TITLE = { The ice age ecologist: Testing methods for reserve prioritization during the last global warming },
    JOURNAL = { Global Ecology and Biogeography },
    YEAR = { 2013 },
    VOLUME = { 22 },
    PAGES = { 289-301 },
    NUMBER = { 3 },
    ABSTRACT = { Aim We play the role of an ice age ecologist (IAE) charged with conserving biodiversity during the climate changes accompanying the last deglaciation. We develop reserve-selection strategies for the IAE and check them against rankings based on modern data. Location Northern and eastern North America. Methods Three reserve-selection strategies are developed. (1) Abiotic: the IAE uses no information about species-climate relationships, instead maximizing the climatic and geographic dispersion of reserves. (2) Species distribution models (SDMs): the IAE uses boosted-regression trees calibrated against pollen data and CCSM3 palaeoclimatic simulations from 21 to 15 ka bp to predict modern taxon distributions, then uses these as input to the Zonation reserve-ranking program. (3) Rank-and-regress: regression models are used to identify climatic predictors of zonation rankings. All strategies are assessed against a Zonation ranking based on modern pollen distributions. Analysis units are ecoregions and grid cells. Results The abiotic strategy has a negative or no correlation between predicted and actual rankings. The SDM-based strategy fares better, with a significantly positive area-corrected correlation (r= 0.474, P < 0.001) between predicted and actual rankings. Predictive ability drops when grid cells are the analysis unit (r= 0.217, P = 0.058). Predictive ability for the rank-and-regress strategy is similar to the SDM results. Main conclusions For the IAE, SDMs improve the predictive ability of reserve-selection strategies. However, predictive ability is limited overall, probably due to shifted realized niches during past no-analogue climates, new species interactions as species responded individually to climate change, and other environmental changes not included in the model. Twenty-first-century conservation planning also faces these challenges, and is further complicated by other anthropogenic impacts. The IAE's limited success does not preclude the use of climate scenarios and niche-based SDMs when developing adaptation strategies, but suggests that such tools offer at best only a rough guide to identifying possible areas of future conservation value. © 2012 Blackwell Publishing Ltd. },
    COMMENT = { Cited By (since 1996): 1 Export Date: 28 February 2013 Source: Scopus CODEN: GEBIF doi: 10.1111/j.1466-8238.2012.00760.x },
    ISSN = { 1466822X (ISSN) },
    KEYWORDS = { Climate change, Niche models, No-analogue climates, Palaeoecology, Pollen, Reserve selection, Species distribution models, Zonation },
    OWNER = { Luc },
    TIMESTAMP = { 2013.02.28 },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-84873955627&partnerID=40&md5=90b4f9c1c884fac08f28dc35e6828f3c },
}

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