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Title: The ice age ecologist: testing methods for reserve prioritization during the last global warming: Reserve selection and the ice age ecologist

Abstract

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. 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. 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. For the IAE, SDMsmore » 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.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8]
  1. Univ. of Wisconsin, Madison, WI (United States). Dept. of Geography, and Center for Climatic Research
  2. Univ. of British Columbia, Vancouver, BC (Canada). Dept. of Zoology
  3. Univ. of Wisconsin, Madison, WI (United States). Dept. of Geography, and Center for Climatic Research; Point Reyes Bird Observatory, Petaluma, CA (United States)
  4. Univ. of British Columbia, Vancouver, BC (Canada). Dept. of Zoology; Univ. de Sherbrooke, Sherbrooke, Québec (Canada). Dépt. de Biologie
  5. Univ. of Notre Dame, IN (United States). Dept. of Biological Sciences
  6. Univ. of Wisconsin, Madison, WI (United States). Center for Climatic Research ; Univ. of Wisconsin, Madison, WI (United States). Dept. of Atmospheric and Ocean Sciences
  7. National Center for Atmospheric Research, Boulder, CO (United States)
  8. Univ. of Wisconsin, Madison, WI (United States). Center for Climatic Research, and Dept. of Atmospheric and Ocean Sciences
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1565076
Resource Type:
Accepted Manuscript
Journal Name:
Global Ecology and Biogeography
Additional Journal Information:
Journal Volume: 22; Journal Issue: 3; Journal ID: ISSN 1466-822X
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Environmental Sciences & Ecology; Physical Geography

Citation Formats

Williams, John W., Kharouba, Heather M., Veloz, Sam, Vellend, Mark, McLachlan, Jason, Liu, Zhengyu, Otto-Bliesner, Bette, and He, Feng. The ice age ecologist: testing methods for reserve prioritization during the last global warming: Reserve selection and the ice age ecologist. United States: N. p., 2012. Web. doi:10.1111/j.1466-8238.2012.00760.x.
Williams, John W., Kharouba, Heather M., Veloz, Sam, Vellend, Mark, McLachlan, Jason, Liu, Zhengyu, Otto-Bliesner, Bette, & He, Feng. The ice age ecologist: testing methods for reserve prioritization during the last global warming: Reserve selection and the ice age ecologist. United States. doi:10.1111/j.1466-8238.2012.00760.x.
Williams, John W., Kharouba, Heather M., Veloz, Sam, Vellend, Mark, McLachlan, Jason, Liu, Zhengyu, Otto-Bliesner, Bette, and He, Feng. Tue . "The ice age ecologist: testing methods for reserve prioritization during the last global warming: Reserve selection and the ice age ecologist". United States. doi:10.1111/j.1466-8238.2012.00760.x. https://www.osti.gov/servlets/purl/1565076.
@article{osti_1565076,
title = {The ice age ecologist: testing methods for reserve prioritization during the last global warming: Reserve selection and the ice age ecologist},
author = {Williams, John W. and Kharouba, Heather M. and Veloz, Sam and Vellend, Mark and McLachlan, Jason and Liu, Zhengyu and Otto-Bliesner, Bette and He, Feng},
abstractNote = {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. 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. 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. 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.},
doi = {10.1111/j.1466-8238.2012.00760.x},
journal = {Global Ecology and Biogeography},
number = 3,
volume = 22,
place = {United States},
year = {2012},
month = {4}
}

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