The Impact of Climate Change on Indigenous Arabica Coffee (Coffea arabica): Predicting Future Trends and Identifying Priorities
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{"title"=>"The Impact of Climate Change on Indigenous Arabica Coffee (Coffea arabica): Predicting Future Trends and Identifying Priorities", "type"=>"journal", "authors"=>[{"first_name"=>"Aaron P.", "last_name"=>"Davis", "scopus_author_id"=>"7404296758"}, {"first_name"=>"Tadesse Woldemariam", "last_name"=>"Gole", "scopus_author_id"=>"23967708000"}, {"first_name"=>"Susana", "last_name"=>"Baena", "scopus_author_id"=>"55467526500"}, {"first_name"=>"Justin", "last_name"=>"Moat", "scopus_author_id"=>"6507046506"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84868689279", "doi"=>"10.1371/journal.pone.0047981", "pui"=>"366024956", "pmid"=>"23144840", "scopus"=>"2-s2.0-84868689279", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "arxiv"=>"e47981"}, "id"=>"798af5b3-a757-3937-8502-47bcd2b20f0d", "abstract"=>"Precise modelling of the influence of climate change on Arabica coffee is limited; there are no data available for indigenous populations of this species. In this study we model the present and future predicted distribution of indigenous Arabica, and identify priorities in order to facilitate appropriate decision making for conservation, monitoring and future research. Using distribution data we perform bioclimatic modelling and examine future distribution with the HadCM3 climate model for three emission scenarios (A1B, A2A, B2A) over three time intervals (2020, 2050, 2080). The models show a profoundly negative influence on indigenous Arabica. In a locality analysis the most favourable outcome is a c. 65% reduction in the number of pre-existing bioclimatically suitable localities, and at worst an almost 100% reduction, by 2080. In an area analysis the most favourable outcome is a 38% reduction in suitable bioclimatic space, and the least favourable a c. 90% reduction, by 2080. Based on known occurrences and ecological tolerances of Arabica, bioclimatic unsuitability would place populations in peril, leading to severe stress and a high risk of extinction. This study establishes a fundamental baseline for assessing the consequences of climate change on wild populations of Arabica coffee. Specifically, it: (1) identifies and categorizes localities and areas that are predicted to be under threat from climate change now and in the short- to medium-term (2020-2050), representing assessment priorities for ex situ conservation; (2) identifies 'core localities' that could have the potential to withstand climate change until at least 2080, and therefore serve as long-term in situ storehouses for coffee genetic resources; (3) provides the location and characterization of target locations (populations) for on-the-ground monitoring of climate change influence. Arabica coffee is confimed as a climate sensitivite species, supporting data and inference that existing plantations will be neagtively impacted by climate change.", "link"=>"http://www.mendeley.com/research/impact-climate-change-indigenous-arabica-coffee-coffea-arabica-predicting-future-trends-identifying-4", "reader_count"=>279, "reader_count_by_academic_status"=>{"Unspecified"=>10, "Professor > Associate Professor"=>10, "Researcher"=>74, "Student > Doctoral Student"=>19, "Student > Ph. D. Student"=>45, "Student > Postgraduate"=>15, "Student > Master"=>52, "Other"=>11, "Student > Bachelor"=>29, "Lecturer"=>5, "Lecturer > Senior Lecturer"=>1, "Professor"=>8}, "reader_count_by_user_role"=>{"Unspecified"=>10, "Professor > Associate Professor"=>10, "Researcher"=>74, "Student > Doctoral Student"=>19, "Student > Ph. D. Student"=>45, "Student > Postgraduate"=>15, "Student > Master"=>52, "Other"=>11, "Student > Bachelor"=>29, "Lecturer"=>5, "Lecturer > Senior Lecturer"=>1, "Professor"=>8}, "reader_count_by_subject_area"=>{"Unspecified"=>15, "Agricultural and Biological Sciences"=>127, "Business, Management and Accounting"=>5, "Chemistry"=>4, "Earth and Planetary Sciences"=>12, "Economics, Econometrics and Finance"=>5, "Engineering"=>3, "Environmental Science"=>80, "Biochemistry, Genetics and Molecular Biology"=>7, "Materials Science"=>1, "Medicine and Dentistry"=>4, "Physics and Astronomy"=>1, "Social Sciences"=>15}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Social Sciences"=>{"Social Sciences"=>15}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Unspecified"=>{"Unspecified"=>15}, "Environmental Science"=>{"Environmental Science"=>80}, "Engineering"=>{"Engineering"=>3}, "Chemistry"=>{"Chemistry"=>4}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>12}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>5}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>127}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>5}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>7}}, "reader_count_by_country"=>{"Colombia"=>2, "United States"=>4, "Japan"=>1, "United Kingdom"=>4, "Portugal"=>2, "Switzerland"=>1, "Costa Rica"=>2, "Sweden"=>1, "South Korea"=>1, "Czech Republic"=>1, "Belgium"=>1, "China"=>1, "Finland"=>2, "Brazil"=>5, "Mexico"=>3, "Italy"=>1, "Uganda"=>1, "France"=>2}, "group_count"=>15}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/546810"], "description"=>"<p>Predicted climate change outcomes for indigenous Arabica for the year intervals 2020, 2050 and 2080. Stacked bar-charts based on area analysis maps (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047981#pone-0047981-g005\" target=\"_blank\">Figure 5</a>).</p>", "links"=>[], "tags"=>["plant biology", "ecology", "computer science"], "article_id"=>217306, "categories"=>["Information And Computing Sciences", "Ecology", "Plant Biology"], "users"=>["Aaron P. Davis", "Tadesse Woldemariam Gole", "Susana Baena", "Justin Moat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0047981.g006", "stats"=>{"downloads"=>1, "page_views"=>43, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Area_analysis_overview_/217306", "title"=>"Area analysis overview.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 20:39:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/546214"], "description"=>"<p>Green dots show recorded data-points. Coloured areas (yellow to red) show predicted distribution based on MaxEnt modelling (see internal legend). A context map is given in the top left hand corner.</p>", "links"=>[], "tags"=>["plant biology", "ecology", "computer science"], "article_id"=>216707, "categories"=>["Information And Computing Sciences", "Ecology", "Plant Biology"], "users"=>["Aaron P. Davis", "Tadesse Woldemariam Gole", "Susana Baena", "Justin Moat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0047981.g001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_and_actual_distribution_of_indigenous_Arabica_/216707", "title"=>"Predicted and actual distribution of indigenous Arabica.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 20:36:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/546578"], "description"=>"<p>Predicted climate change outcomes for indigenous Arabica localities (349 in total) for the year intervals 2000, 2020, 2050 and 2080. Histograms for actual predicted values, under each scenario. Dashed line and red text indicate thresholds (68%, 95%, 100%, of the 2000 models). This figure provides finer-scale detail than <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047981#pone-0047981-g003\" target=\"_blank\">Figure 3</a>, including the subtle shifts around the thresholds that are evident in the locality analysis. For example in scenario B2A, in 2000 there are a high proportion of localities in optimum bioclimatic space (0.6 and 0.65), but by 2080 most of the localities are outside of all suitable bioclimatic space, with a small number of localities (‘core localites’) still occupying optimal bioclimatic space.</p>", "links"=>[], "tags"=>["overview"], "article_id"=>217076, "categories"=>["Information And Computing Sciences", "Ecology", "Plant Biology"], "users"=>["Aaron P. Davis", "Tadesse Woldemariam Gole", "Susana Baena", "Justin Moat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0047981.g004", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Locality_analysis_overview_II_/217076", "title"=>"Locality analysis overview II.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 20:38:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/547057"], "description"=>"<p>Number of (unique) localities in each threshold class for each climate change (emission) scenario and date.</p>", "links"=>[], "tags"=>["localities"], "article_id"=>217550, "categories"=>["Information And Computing Sciences", "Ecology", "Plant Biology"], "users"=>["Aaron P. Davis", "Tadesse Woldemariam Gole", "Susana Baena", "Justin Moat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0047981.t001", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_unique_localities_in_each_threshold_class_for_each_climate_change_emission_scenario_and_date_/217550", "title"=>"Number of (unique) localities in each threshold class for each climate change (emission) scenario and date.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-19 20:41:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/546897"], "description"=>"<p>Point size and colour represent the total predicted score for each locality across all scenarios and time intervals (until 2080). Large dots (high score) represent ‘core localities’, i.e. those that predicted to withstand climate change until at least 2080. See internal legend for further details. Protected area data from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047981#pone.0047981-IUCN1\" target=\"_blank\">[70]</a>. Dedicated coffee reserves: Yayu = Yayu Coffee Forest UNESCO MAN Biosphere Reserve (<a href=\"http://www.unesco.org/mabdb/br/brdir/directory/biores.asp?code=ETH02&mode=all\" target=\"_blank\">http://www.unesco.org/mabdb/br/brdir/directory/biores.asp?code=ETH02&mode=all</a>. Accessed 2012 May 10), included within the Yayu National Forest Priority Area (NFPA) (<a href=\"http://www.ecff.org.et/component/content/article/10-yayu/6-yayu-coffee-forest-biosphere-reserve.html\" target=\"_blank\">http://www.ecff.org.et/component/content/article/10-yayu/6-yayu-coffee-forest-biosphere-reserve.html</a>. Accessed 2012 May 10); Kafa = Kafa Coffee Biosphere Reserve UNESCO Biosphere Reserve (<a href=\"http://www.kafa-biosphere.com/\" target=\"_blank\">http://www.kafa-biosphere.com/</a>. Accessed 2012 May 10). Note. Controlled Hunting Areas not shown.</p>", "links"=>[], "tags"=>["predictions", "superimposed", "protected", "areas"], "article_id"=>217390, "categories"=>["Information And Computing Sciences", "Ecology", "Plant Biology"], "users"=>["Aaron P. Davis", "Tadesse Woldemariam Gole", "Susana Baena", "Justin Moat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0047981.g007", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Locality_analysis_predictions_superimposed_on_main_protected_areas_in_the_study_region_/217390", "title"=>"Locality analysis predictions superimposed on main protected areas in the study region.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 20:40:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/546676"], "description"=>"<p>Predicted climate change outcomes for indigenous Arabica for the year intervals 2020, 2050 and 2080. Thresholds (of bioclimatic suitability) applied at 68% (optimal), 95% (intermediate) and 100% (marginal). Black dots show historical and present day localities.</p>", "links"=>[], "tags"=>["plant biology", "ecology", "computer science"], "article_id"=>217175, "categories"=>["Information And Computing Sciences", "Ecology", "Plant Biology"], "users"=>["Aaron P. Davis", "Tadesse Woldemariam Gole", "Susana Baena", "Justin Moat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0047981.g005", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Area_analysis_maps_/217175", "title"=>"Area analysis maps.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 20:39:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/546526"], "description"=>"<p>Predicted climate change outcomes for indigenous Arabica localities for the year interval 2000, 2020, 2050 and 2080. Stacked bar-charts based on <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047981#pone-0047981-t001\" target=\"_blank\">Table 1</a>. Green = optimal [bioclimatic] localities (68%); yellow = intermediate (suboptimal) [bioclimatic] localities (95%); red = marginal (extreme) [bioclimatic] localities (100%); grey = unsuitable bioclimatic localities.</p>", "links"=>[], "tags"=>["overview"], "article_id"=>217025, "categories"=>["Information And Computing Sciences", "Ecology", "Plant Biology"], "users"=>["Aaron P. Davis", "Tadesse Woldemariam Gole", "Susana Baena", "Justin Moat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0047981.g003", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Locality_analysis_overview_I_/217025", "title"=>"Locality analysis overview I.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 20:38:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/546432"], "description"=>"<p><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047981#pone-0047981-g002\" target=\"_blank\">Figure 2A</a>. Green dots show recorded data-points. Coloured areas (yellow to red) show predicted distribution based on MaxEnt modelling (see internal legend). Highest predicted area (dark red) indicates ‘core region’. <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047981#pone-0047981-g002\" target=\"_blank\">Figure 2B</a>. The same map with thresholds of bioclimatic suitability applied at 68% (optimal), 95% (intermediate) and 100% (marginal) to the localities. Prediction values for each locality are represented by colour and size (see internal legend) with values for low predictions labelled in red, superimposed on predicted surface (space) according to the area analysis. Localities with the smallest (dark green) circles represent ‘core localities’; highest (optimal) predicted area (green) indicates the ‘core region’. See main text for further details.</p>", "links"=>[], "tags"=>["plant biology", "ecology", "computer science"], "article_id"=>216927, "categories"=>["Information And Computing Sciences", "Ecology", "Plant Biology"], "users"=>["Aaron P. Davis", "Tadesse Woldemariam Gole", "Susana Baena", "Justin Moat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0047981.g002", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_and_actual_distribution_of_indigenous_Arabica_/216927", "title"=>"Predicted and actual distribution of indigenous Arabica.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 20:37:40"}

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Relative Metric

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