Data

Quantile-based monthly climate extreme variables and predicted plant species distributions (37) across Victoria, southeast Australia

Name: Quantile-based monthly climate extreme variables and predicted plant species distributions (37) across Victoria, southeast Australia
Published: 2021-01-14

Commonwealth Scientific and Industrial Research Organisation

Stewart, Stephen ; Elith, Jane ; Fedrigo, Melissa ; Kasel, Sabine ; Roxburgh, Stephen ; Bennett, Lauren ; Chick, Matt ; Fairman, Tom ; Leonard, Steven ; Kohout, Michele ; Cripps, Jemma ; Durkin, Louise ; Nitschke, Craig

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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/b49k-9h77&rft.title=Quantile-based monthly climate extreme variables and predicted plant species distributions (37) across Victoria, southeast Australia&rft.identifier=https://doi.org/10.25919/b49k-9h77&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=This collection includes each of the climate variables (including quantile-based extremes) and predicted plant species distributions (37) generated as part of the manuscript titled 'Climate extreme variables generated using monthly time-series data improve predicted distributions of plant species' (Stewart et al. 2020a; doi: 10.1111/ecog.05253).\nLineage: Climate variables are generated using 39 years of monthly maximum temperature (Stewart & Nitschke 2017), minimum temperature (Stewart & Nitschke 2018) and precipitation data (Stewart, et al. 2020b). Annual calculations for maximum temperature of the hottest month (BIO5), minimum temperature of the coldest month (BIO6), and precipitation of the driest quarter (BIO17) were used to quantify 'base climate' (long-term means), variability (standard deviations) and extremes of varying return intervals (defined using quantiles) based on historical observations. A tutorial, with R code, for producing these layers is provided in the supporting information to the manuscript (SDMExtremes_AppendixS2.pdf).\n\nSpecies distribution models were fitted and predicted for 37 plant species across Victoria using boosted regression trees, following the procedures detailed in the published manuscript. Images are provided for base climate, variability and extreme (with 1 in 15 year return interval) models. All cross validation results are provided in the supporting information to the manuscript (SDMExtremes_Appendix_S4.xlsx).&rft.creator=Stewart, Stephen &rft.creator=Elith, Jane &rft.creator=Fedrigo, Melissa &rft.creator=Kasel, Sabine &rft.creator=Roxburgh, Stephen &rft.creator=Bennett, Lauren &rft.creator=Chick, Matt &rft.creator=Fairman, Tom &rft.creator=Leonard, Steven &rft.creator=Kohout, Michele &rft.creator=Cripps, Jemma &rft.creator=Durkin, Louise &rft.creator=Nitschke, Craig &rft.date=2021&rft.edition=v1&rft.relation=http://doi.org/10.1111/ecog.05253&rft.relation=https://doi.org/10.1016/j.foreco.2018.10.048&rft.relation=https://doi.org/10.1111/jvs.12649&rft.relation=https://doi.org/10.1111/jvs.12575&rft.relation=https://doi.org/10.3390/rs11010093&rft.relation=https://doi.org/10.1002/eap.1399&rft.relation=https://doi.org/10.2989/20702620.2017.1318344&rft.relation=https://link.springer.com/article/10.1007/s10980-017-0526-7&rft.relation=https://www.ffm.vic.gov.au/__data/assets/pdf_file/0007/21121/Report-96-Foothills-fire-and-biota-report.pdf&rft.relation=https://www.ari.vic.gov.au/__data/assets/pdf_file/0024/93138/ARI-Technical-Report-278-Targeted-surveys-to-improve-Leadbeaters-Possum-conservation.pdf&rft.coverage=westlimit=140.95999999999998; southlimit=-39.16; eastlimit=149.98; northlimit=-33.980000000000004; projection=WGS84&rft_rights=Creative Commons Attribution 4.0 International Licence https://creativecommons.org/licenses/by/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO, The University of Melbourne 2020.&rft_subject=Victoria&rft_subject=species distribution modelling&rft_subject=time-series&rft_subject=plant species&rft_subject=climate extremes&rft_subject=climate variability&rft_subject=Biogeography and phylogeography&rft_subject=Evolutionary biology&rft_subject=BIOLOGICAL SCIENCES&rft_subject=Ecological applications not elsewhere classified&rft_subject=Ecological applications&rft_subject=ENVIRONMENTAL SCIENCES&rft.type=dataset&rft.language=English Access the data

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Creative Commons Attribution 4.0 International Licence
https://creativecommons.org/licenses/by/4.0/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO, The University of Melbourne 2020.

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Brief description

This collection includes each of the climate variables (including quantile-based extremes) and predicted plant species distributions (37) generated as part of the manuscript titled 'Climate extreme variables generated using monthly time-series data improve predicted distributions of plant species' (Stewart et al. 2020a; doi: 10.1111/ecog.05253).
Lineage: Climate variables are generated using 39 years of monthly maximum temperature (Stewart & Nitschke 2017), minimum temperature (Stewart & Nitschke 2018) and precipitation data (Stewart, et al. 2020b). Annual calculations for maximum temperature of the hottest month (BIO5), minimum temperature of the coldest month (BIO6), and precipitation of the driest quarter (BIO17) were used to quantify 'base climate' (long-term means), variability (standard deviations) and extremes of varying return intervals (defined using quantiles) based on historical observations. A tutorial, with R code, for producing these layers is provided in the supporting information to the manuscript (SDMExtremes_AppendixS2.pdf).

Species distribution models were fitted and predicted for 37 plant species across Victoria using boosted regression trees, following the procedures detailed in the published manuscript. Images are provided for base climate, variability and extreme (with 1 in 15 year return interval) models. All cross validation results are provided in the supporting information to the manuscript (SDMExtremes_Appendix_S4.xlsx).

Available: 2021-01-14

Data time period: 1981-01-01 to 2019-12-31

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Australian Research Council (ARC) : DP 160101003

Identifiers

DOI : 10.25919/B49K-9H77
Handle : 102.100.100/388162
URL : data.csiro.au/collection/csiro:48369

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